Overview

Dataset statistics

Number of variables29
Number of observations97
Missing cells95
Missing cells (%)3.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory22.1 KiB
Average record size in memory233.3 B

Variable types

Numeric9
Categorical20

Alerts

type has constant value "regular" Constant
airdate has constant value "2020-12-01" Constant
url has a high cardinality: 97 distinct values High cardinality
name has a high cardinality: 92 distinct values High cardinality
_embedded_show_url has a high cardinality: 67 distinct values High cardinality
_embedded_show_name has a high cardinality: 67 distinct values High cardinality
_embedded_show_officialSite has a high cardinality: 63 distinct values High cardinality
_embedded_show_summary has a high cardinality: 59 distinct values High cardinality
_links_self_href has a high cardinality: 97 distinct values High cardinality
runtime is highly correlated with _embedded_show_runtime and 1 other fieldsHigh correlation
_embedded_show_runtime is highly correlated with runtime and 1 other fieldsHigh correlation
_embedded_show_averageRuntime is highly correlated with runtime and 1 other fieldsHigh correlation
runtime is highly correlated with _embedded_show_runtime and 1 other fieldsHigh correlation
_embedded_show_runtime is highly correlated with runtime and 1 other fieldsHigh correlation
_embedded_show_averageRuntime is highly correlated with runtime and 1 other fieldsHigh correlation
runtime is highly correlated with _embedded_show_runtime and 1 other fieldsHigh correlation
_embedded_show_runtime is highly correlated with runtime and 1 other fieldsHigh correlation
_embedded_show_averageRuntime is highly correlated with runtime and 1 other fieldsHigh correlation
_embedded_show_officialSite is highly correlated with _embedded_show_summary and 16 other fieldsHigh correlation
summary is highly correlated with url and 5 other fieldsHigh correlation
_embedded_show_summary is highly correlated with _embedded_show_officialSite and 14 other fieldsHigh correlation
_embedded_show_type is highly correlated with _embedded_show_officialSite and 10 other fieldsHigh correlation
_embedded_show_status is highly correlated with _embedded_show_officialSite and 10 other fieldsHigh correlation
url is highly correlated with _embedded_show_officialSite and 18 other fieldsHigh correlation
image is highly correlated with _embedded_show_officialSite and 18 other fieldsHigh correlation
_embedded_show_ended is highly correlated with _embedded_show_officialSite and 10 other fieldsHigh correlation
_embedded_show_name is highly correlated with _embedded_show_officialSite and 16 other fieldsHigh correlation
_embedded_show_premiered is highly correlated with _embedded_show_officialSite and 9 other fieldsHigh correlation
name is highly correlated with url and 4 other fieldsHigh correlation
airdate is highly correlated with _embedded_show_officialSite and 18 other fieldsHigh correlation
airtime is highly correlated with _embedded_show_officialSite and 10 other fieldsHigh correlation
_links_self_href is highly correlated with _embedded_show_officialSite and 18 other fieldsHigh correlation
_embedded_show_genres is highly correlated with _embedded_show_officialSite and 12 other fieldsHigh correlation
type is highly correlated with _embedded_show_officialSite and 18 other fieldsHigh correlation
_embedded_show_url is highly correlated with _embedded_show_officialSite and 16 other fieldsHigh correlation
_embedded_show_language is highly correlated with _embedded_show_officialSite and 10 other fieldsHigh correlation
_embedded_show_dvdCountry is highly correlated with _embedded_show_officialSite and 12 other fieldsHigh correlation
airstamp is highly correlated with _embedded_show_officialSite and 10 other fieldsHigh correlation
id is highly correlated with url and 21 other fieldsHigh correlation
url is highly correlated with id and 25 other fieldsHigh correlation
name is highly correlated with id and 19 other fieldsHigh correlation
season is highly correlated with url and 10 other fieldsHigh correlation
number is highly correlated with url and 15 other fieldsHigh correlation
airtime is highly correlated with id and 17 other fieldsHigh correlation
airstamp is highly correlated with id and 20 other fieldsHigh correlation
runtime is highly correlated with id and 17 other fieldsHigh correlation
image is highly correlated with id and 24 other fieldsHigh correlation
summary is highly correlated with url and 17 other fieldsHigh correlation
_embedded_show_id is highly correlated with id and 18 other fieldsHigh correlation
_embedded_show_url is highly correlated with id and 25 other fieldsHigh correlation
_embedded_show_name is highly correlated with id and 25 other fieldsHigh correlation
_embedded_show_type is highly correlated with id and 19 other fieldsHigh correlation
_embedded_show_language is highly correlated with id and 21 other fieldsHigh correlation
_embedded_show_genres is highly correlated with id and 21 other fieldsHigh correlation
_embedded_show_status is highly correlated with id and 16 other fieldsHigh correlation
_embedded_show_runtime is highly correlated with id and 19 other fieldsHigh correlation
_embedded_show_averageRuntime is highly correlated with id and 18 other fieldsHigh correlation
_embedded_show_premiered is highly correlated with id and 23 other fieldsHigh correlation
_embedded_show_ended is highly correlated with id and 16 other fieldsHigh correlation
_embedded_show_officialSite is highly correlated with id and 25 other fieldsHigh correlation
_embedded_show_weight is highly correlated with url and 17 other fieldsHigh correlation
_embedded_show_dvdCountry is highly correlated with id and 13 other fieldsHigh correlation
_embedded_show_summary is highly correlated with id and 25 other fieldsHigh correlation
_embedded_show_updated is highly correlated with id and 16 other fieldsHigh correlation
_links_self_href is highly correlated with id and 25 other fieldsHigh correlation
runtime has 5 (5.2%) missing values Missing
image has 71 (73.2%) missing values Missing
_embedded_show_runtime has 16 (16.5%) missing values Missing
_embedded_show_averageRuntime has 3 (3.1%) missing values Missing
url is uniformly distributed Uniform
name is uniformly distributed Uniform
image is uniformly distributed Uniform
_links_self_href is uniformly distributed Uniform
id has unique values Unique
url has unique values Unique
_links_self_href has unique values Unique
_embedded_show_weight has 1 (1.0%) zeros Zeros

Reproduction

Analysis started2022-05-10 01:58:50.636464
Analysis finished2022-05-10 01:59:30.626687
Duration39.99 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

id
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct97
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2014731.062
Minimum1939481
Maximum2318095
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size904.0 B
2022-05-09T20:59:30.719919image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1939481
5-th percentile1964246.8
Q11976152
median1978545
Q32033286
95-th percentile2181573.6
Maximum2318095
Range378614
Interquartile range (IQR)57134

Descriptive statistics

Standard deviation78343.18031
Coefficient of variation (CV)0.03888518016
Kurtosis6.66796281
Mean2014731.062
Median Absolute Deviation (MAD)9332
Skewness2.584968683
Sum195428913
Variance6137653901
MonotonicityNot monotonic
2022-05-09T20:59:30.843999image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19798241
 
1.0%
19760261
 
1.0%
20332901
 
1.0%
20332891
 
1.0%
20332881
 
1.0%
20332871
 
1.0%
20332861
 
1.0%
20332851
 
1.0%
20332841
 
1.0%
19882991
 
1.0%
Other values (87)87
89.7%
ValueCountFrequency (%)
19394811
1.0%
19568441
1.0%
19600281
1.0%
19604961
1.0%
19639101
1.0%
19643311
1.0%
19645651
1.0%
19685461
1.0%
19685471
1.0%
19692111
1.0%
ValueCountFrequency (%)
23180951
1.0%
23151161
1.0%
23110171
1.0%
22877851
1.0%
22033961
1.0%
21761181
1.0%
21650051
1.0%
21614141
1.0%
20931321
1.0%
20931311
1.0%

url
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct97
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size904.0 B
https://www.tvmaze.com/episodes/1979824/sim-for-you-4x16-chanyeols-episode-16
 
1
https://www.tvmaze.com/episodes/1976026/twisted-fate-of-love-1x15-episode-15
 
1
https://www.tvmaze.com/episodes/2033290/33-rebenka-1x07-7-seria
 
1
https://www.tvmaze.com/episodes/2033289/33-rebenka-1x06-6-seria
 
1
https://www.tvmaze.com/episodes/2033288/33-rebenka-1x05-5-seria
 
1
Other values (92)
92 

Length

Max length136
Median length95
Mean length76.49484536
Min length58

Characters and Unicode

Total characters7420
Distinct characters39
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique97 ?
Unique (%)100.0%

Sample

1st rowhttps://www.tvmaze.com/episodes/1979824/sim-for-you-4x16-chanyeols-episode-16
2nd rowhttps://www.tvmaze.com/episodes/1979222/kotiki-1x02-seria-2
3rd rowhttps://www.tvmaze.com/episodes/2008027/lab-s-antonom-belaevym-2x06-lolita
4th rowhttps://www.tvmaze.com/episodes/1964565/core-sense-1x09-episode-9
5th rowhttps://www.tvmaze.com/episodes/2052503/wu-shen-zhu-zai-1x80-episode-80

Common Values

ValueCountFrequency (%)
https://www.tvmaze.com/episodes/1979824/sim-for-you-4x16-chanyeols-episode-161
 
1.0%
https://www.tvmaze.com/episodes/1976026/twisted-fate-of-love-1x15-episode-151
 
1.0%
https://www.tvmaze.com/episodes/2033290/33-rebenka-1x07-7-seria1
 
1.0%
https://www.tvmaze.com/episodes/2033289/33-rebenka-1x06-6-seria1
 
1.0%
https://www.tvmaze.com/episodes/2033288/33-rebenka-1x05-5-seria1
 
1.0%
https://www.tvmaze.com/episodes/2033287/33-rebenka-1x04-4-seria1
 
1.0%
https://www.tvmaze.com/episodes/2033286/33-rebenka-1x03-3-seria1
 
1.0%
https://www.tvmaze.com/episodes/2033285/33-rebenka-1x02-2-seria1
 
1.0%
https://www.tvmaze.com/episodes/2033284/33-rebenka-1x01-1-seria1
 
1.0%
https://www.tvmaze.com/episodes/1988299/nwa-shockwave-1x01-episode-11
 
1.0%
Other values (87)87
89.7%

Length

2022-05-09T20:59:30.994678image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://www.tvmaze.com/episodes/1979824/sim-for-you-4x16-chanyeols-episode-161
 
1.0%
https://www.tvmaze.com/episodes/1979222/kotiki-1x02-seria-21
 
1.0%
https://www.tvmaze.com/episodes/2008027/lab-s-antonom-belaevym-2x06-lolita1
 
1.0%
https://www.tvmaze.com/episodes/1964565/core-sense-1x09-episode-91
 
1.0%
https://www.tvmaze.com/episodes/2052503/wu-shen-zhu-zai-1x80-episode-801
 
1.0%
https://www.tvmaze.com/episodes/2315116/sono-koi-mousukoshi-atatamemasuka-1x05-episode-51
 
1.0%
https://www.tvmaze.com/episodes/1973538/please-wait-brother-1x17-episode-171
 
1.0%
https://www.tvmaze.com/episodes/1973539/please-wait-brother-1x18-episode-181
 
1.0%
https://www.tvmaze.com/episodes/1984264/fearless-whispers-1x51-episode-511
 
1.0%
https://www.tvmaze.com/episodes/2082171/ling-jian-zun-4x28-di128ji1
 
1.0%
Other values (87)87
89.7%

Most occurring characters

ValueCountFrequency (%)
e622
 
8.4%
-542
 
7.3%
/485
 
6.5%
s484
 
6.5%
t451
 
6.1%
o391
 
5.3%
w326
 
4.4%
a323
 
4.4%
i282
 
3.8%
1273
 
3.7%
Other values (29)3241
43.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter4962
66.9%
Decimal Number1140
 
15.4%
Other Punctuation776
 
10.5%
Dash Punctuation542
 
7.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e622
12.5%
s484
 
9.8%
t451
 
9.1%
o391
 
7.9%
w326
 
6.6%
a323
 
6.5%
i282
 
5.7%
m258
 
5.2%
p250
 
5.0%
d206
 
4.2%
Other values (15)1369
27.6%
Decimal Number
ValueCountFrequency (%)
1273
23.9%
0154
13.5%
2147
12.9%
9124
10.9%
3118
10.4%
787
 
7.6%
880
 
7.0%
657
 
5.0%
450
 
4.4%
550
 
4.4%
Other Punctuation
ValueCountFrequency (%)
/485
62.5%
.194
 
25.0%
:97
 
12.5%
Dash Punctuation
ValueCountFrequency (%)
-542
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin4962
66.9%
Common2458
33.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e622
12.5%
s484
 
9.8%
t451
 
9.1%
o391
 
7.9%
w326
 
6.6%
a323
 
6.5%
i282
 
5.7%
m258
 
5.2%
p250
 
5.0%
d206
 
4.2%
Other values (15)1369
27.6%
Common
ValueCountFrequency (%)
-542
22.1%
/485
19.7%
1273
11.1%
.194
 
7.9%
0154
 
6.3%
2147
 
6.0%
9124
 
5.0%
3118
 
4.8%
:97
 
3.9%
787
 
3.5%
Other values (4)237
9.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII7420
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e622
 
8.4%
-542
 
7.3%
/485
 
6.5%
s484
 
6.5%
t451
 
6.1%
o391
 
5.3%
w326
 
4.4%
a323
 
4.4%
i282
 
3.8%
1273
 
3.7%
Other values (29)3241
43.7%

name
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION
UNIFORM

Distinct92
Distinct (%)94.8%
Missing0
Missing (%)0.0%
Memory size904.0 B
Episode 9
 
2
Episode 12
 
2
Episode 17
 
2
Episode 18
 
2
Episode 1
 
2
Other values (87)
87 

Length

Max length61
Median length43
Mean length15.79381443
Min length3

Characters and Unicode

Total characters1532
Distinct characters117
Distinct categories10 ?
Distinct scripts5 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique87 ?
Unique (%)89.7%

Sample

1st rowChanyeol's Episode 16
2nd rowСерия 2
3rd rowЛолита
4th rowEpisode 9
5th rowEpisode 80

Common Values

ValueCountFrequency (%)
Episode 92
 
2.1%
Episode 122
 
2.1%
Episode 172
 
2.1%
Episode 182
 
2.1%
Episode 12
 
2.1%
Chanyeol's Episode 161
 
1.0%
1 серия1
 
1.0%
9 серия1
 
1.0%
8 серия1
 
1.0%
7 серия1
 
1.0%
Other values (82)82
84.5%

Length

2022-05-09T20:59:31.211264image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
episode27
 
9.7%
серия14
 
5.0%
6
 
2.2%
the5
 
1.8%
14
 
1.4%
wedding3
 
1.1%
123
 
1.1%
63
 
1.1%
a3
 
1.1%
93
 
1.1%
Other values (190)207
74.5%

Most occurring characters

ValueCountFrequency (%)
181
 
11.8%
e107
 
7.0%
i79
 
5.2%
a74
 
4.8%
s74
 
4.8%
o72
 
4.7%
d57
 
3.7%
n56
 
3.7%
r50
 
3.3%
t46
 
3.0%
Other values (107)736
48.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1005
65.6%
Uppercase Letter196
 
12.8%
Space Separator181
 
11.8%
Decimal Number93
 
6.1%
Other Punctuation35
 
2.3%
Other Letter16
 
1.0%
Open Punctuation2
 
0.1%
Close Punctuation2
 
0.1%
Currency Symbol1
 
0.1%
Dash Punctuation1
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e107
 
10.6%
i79
 
7.9%
a74
 
7.4%
s74
 
7.4%
o72
 
7.2%
d57
 
5.7%
n56
 
5.6%
r50
 
5.0%
t46
 
4.6%
l41
 
4.1%
Other values (39)349
34.7%
Uppercase Letter
ValueCountFrequency (%)
E40
20.4%
S19
 
9.7%
B13
 
6.6%
C13
 
6.6%
A13
 
6.6%
H9
 
4.6%
T8
 
4.1%
N7
 
3.6%
D7
 
3.6%
I7
 
3.6%
Other values (22)60
30.6%
Other Letter
ValueCountFrequency (%)
و3
18.8%
ا2
12.5%
1
 
6.2%
1
 
6.2%
ه1
 
6.2%
ل1
 
6.2%
ر1
 
6.2%
گ1
 
6.2%
ب1
 
6.2%
ز1
 
6.2%
Other values (3)3
18.8%
Decimal Number
ValueCountFrequency (%)
127
29.0%
215
16.1%
08
 
8.6%
88
 
8.6%
68
 
8.6%
37
 
7.5%
46
 
6.5%
96
 
6.5%
55
 
5.4%
73
 
3.2%
Other Punctuation
ValueCountFrequency (%)
'9
25.7%
.8
22.9%
&5
14.3%
,4
11.4%
?3
 
8.6%
!3
 
8.6%
"2
 
5.7%
:1
 
2.9%
Space Separator
ValueCountFrequency (%)
181
100.0%
Open Punctuation
ValueCountFrequency (%)
(2
100.0%
Close Punctuation
ValueCountFrequency (%)
)2
100.0%
Currency Symbol
ValueCountFrequency (%)
£1
100.0%
Dash Punctuation
ValueCountFrequency (%)
-1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1032
67.4%
Common315
 
20.6%
Cyrillic169
 
11.0%
Arabic14
 
0.9%
Han2
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e107
 
10.4%
i79
 
7.7%
a74
 
7.2%
s74
 
7.2%
o72
 
7.0%
d57
 
5.5%
n56
 
5.4%
r50
 
4.8%
t46
 
4.5%
l41
 
4.0%
Other values (39)376
36.4%
Cyrillic
ValueCountFrequency (%)
р20
11.8%
с19
11.2%
и19
11.2%
е18
10.7%
а17
10.1%
я14
 
8.3%
о9
 
5.3%
м6
 
3.6%
т6
 
3.6%
к5
 
3.0%
Other values (22)36
21.3%
Common
ValueCountFrequency (%)
181
57.5%
127
 
8.6%
215
 
4.8%
'9
 
2.9%
08
 
2.5%
88
 
2.5%
.8
 
2.5%
68
 
2.5%
37
 
2.2%
46
 
1.9%
Other values (13)38
 
12.1%
Arabic
ValueCountFrequency (%)
و3
21.4%
ا2
14.3%
ه1
 
7.1%
ل1
 
7.1%
ر1
 
7.1%
گ1
 
7.1%
ب1
 
7.1%
ز1
 
7.1%
ی1
 
7.1%
س1
 
7.1%
Han
ValueCountFrequency (%)
1
50.0%
1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1341
87.5%
Cyrillic169
 
11.0%
Arabic14
 
0.9%
None6
 
0.4%
CJK2
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
181
 
13.5%
e107
 
8.0%
i79
 
5.9%
a74
 
5.5%
s74
 
5.5%
o72
 
5.4%
d57
 
4.3%
n56
 
4.2%
r50
 
3.7%
t46
 
3.4%
Other values (58)545
40.6%
Cyrillic
ValueCountFrequency (%)
р20
11.8%
с19
11.2%
и19
11.2%
е18
10.7%
а17
10.1%
я14
 
8.3%
о9
 
5.3%
м6
 
3.6%
т6
 
3.6%
к5
 
3.0%
Other values (22)36
21.3%
Arabic
ValueCountFrequency (%)
و3
21.4%
ا2
14.3%
ه1
 
7.1%
ل1
 
7.1%
ر1
 
7.1%
گ1
 
7.1%
ب1
 
7.1%
ز1
 
7.1%
ی1
 
7.1%
س1
 
7.1%
None
ValueCountFrequency (%)
ö2
33.3%
ü2
33.3%
á1
16.7%
£1
16.7%
CJK
ValueCountFrequency (%)
1
50.0%
1
50.0%

season
Real number (ℝ≥0)

HIGH CORRELATION

Distinct10
Distinct (%)10.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean189.4020619
Minimum1
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size904.0 B
2022-05-09T20:59:31.343573image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile2020
Maximum2020
Range2019
Interquartile range (IQR)1

Descriptive statistics

Standard deviation588.4805055
Coefficient of variation (CV)3.107043819
Kurtosis6.258550389
Mean189.4020619
Median Absolute Deviation (MAD)0
Skewness2.851213037
Sum18372
Variance346309.3054
MonotonicityNot monotonic
2022-05-09T20:59:31.448053image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
170
72.2%
20209
 
9.3%
44
 
4.1%
24
 
4.1%
33
 
3.1%
72
 
2.1%
82
 
2.1%
101
 
1.0%
181
 
1.0%
311
 
1.0%
ValueCountFrequency (%)
170
72.2%
24
 
4.1%
33
 
3.1%
44
 
4.1%
72
 
2.1%
82
 
2.1%
101
 
1.0%
181
 
1.0%
311
 
1.0%
20209
 
9.3%
ValueCountFrequency (%)
20209
 
9.3%
311
 
1.0%
181
 
1.0%
101
 
1.0%
82
 
2.1%
72
 
2.1%
44
 
4.1%
33
 
3.1%
24
 
4.1%
170
72.2%

number
Real number (ℝ≥0)

HIGH CORRELATION

Distinct42
Distinct (%)43.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.51546392
Minimum1
Maximum328
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size904.0 B
2022-05-09T20:59:31.578335image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median11
Q322
95-th percentile98.6
Maximum328
Range327
Interquartile range (IQR)18

Descriptive statistics

Standard deviation49.13037787
Coefficient of variation (CV)1.852895277
Kurtosis22.03667828
Mean26.51546392
Median Absolute Deviation (MAD)8
Skewness4.322280109
Sum2572
Variance2413.794029
MonotonicityNot monotonic
2022-05-09T20:59:31.725830image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
38
 
8.2%
17
 
7.2%
26
 
6.2%
95
 
5.2%
64
 
4.1%
44
 
4.1%
124
 
4.1%
114
 
4.1%
84
 
4.1%
74
 
4.1%
Other values (32)47
48.5%
ValueCountFrequency (%)
17
7.2%
26
6.2%
38
8.2%
44
4.1%
53
 
3.1%
64
4.1%
74
4.1%
84
4.1%
95
5.2%
102
 
2.1%
ValueCountFrequency (%)
3281
1.0%
2871
1.0%
1411
1.0%
1141
1.0%
1051
1.0%
971
1.0%
931
1.0%
801
1.0%
721
1.0%
561
1.0%

type
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size904.0 B
regular
97 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters679
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowregular
2nd rowregular
3rd rowregular
4th rowregular
5th rowregular

Common Values

ValueCountFrequency (%)
regular97
100.0%

Length

2022-05-09T20:59:31.854419image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T20:59:32.012625image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
regular97
100.0%

Most occurring characters

ValueCountFrequency (%)
r194
28.6%
e97
14.3%
g97
14.3%
u97
14.3%
l97
14.3%
a97
14.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter679
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r194
28.6%
e97
14.3%
g97
14.3%
u97
14.3%
l97
14.3%
a97
14.3%

Most occurring scripts

ValueCountFrequency (%)
Latin679
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r194
28.6%
e97
14.3%
g97
14.3%
u97
14.3%
l97
14.3%
a97
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII679
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r194
28.6%
e97
14.3%
g97
14.3%
u97
14.3%
l97
14.3%
a97
14.3%

airdate
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size904.0 B
2020-12-01
97 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters970
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020-12-01
2nd row2020-12-01
3rd row2020-12-01
4th row2020-12-01
5th row2020-12-01

Common Values

ValueCountFrequency (%)
2020-12-0197
100.0%

Length

2022-05-09T20:59:32.075129image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T20:59:32.178217image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
2020-12-0197
100.0%

Most occurring characters

ValueCountFrequency (%)
2291
30.0%
0291
30.0%
-194
20.0%
1194
20.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number776
80.0%
Dash Punctuation194
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2291
37.5%
0291
37.5%
1194
25.0%
Dash Punctuation
ValueCountFrequency (%)
-194
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common970
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2291
30.0%
0291
30.0%
-194
20.0%
1194
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII970
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2291
30.0%
0291
30.0%
-194
20.0%
1194
20.0%

airtime
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct15
Distinct (%)15.5%
Missing0
Missing (%)0.0%
Memory size904.0 B
nan
63 
20:00
13 
06:00
 
6
10:00
 
2
12:00
 
2
Other values (10)
11 

Length

Max length5
Median length3
Mean length3.701030928
Min length3

Characters and Unicode

Total characters359
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)9.3%

Sample

1st row06:00
2nd rownan
3rd rownan
4th row10:00
5th row10:00

Common Values

ValueCountFrequency (%)
nan63
64.9%
20:0013
 
13.4%
06:006
 
6.2%
10:002
 
2.1%
12:002
 
2.1%
20:402
 
2.1%
08:001
 
1.0%
17:001
 
1.0%
17:351
 
1.0%
00:001
 
1.0%
Other values (5)5
 
5.2%

Length

2022-05-09T20:59:32.262806image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nan63
64.9%
20:0013
 
13.4%
06:006
 
6.2%
10:002
 
2.1%
12:002
 
2.1%
20:402
 
2.1%
08:001
 
1.0%
17:001
 
1.0%
17:351
 
1.0%
00:001
 
1.0%
Other values (5)5
 
5.2%

Most occurring characters

ValueCountFrequency (%)
n126
35.1%
091
25.3%
a63
17.5%
:34
 
9.5%
220
 
5.6%
17
 
1.9%
66
 
1.7%
73
 
0.8%
53
 
0.8%
42
 
0.6%
Other values (2)4
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter189
52.6%
Decimal Number136
37.9%
Other Punctuation34
 
9.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
091
66.9%
220
 
14.7%
17
 
5.1%
66
 
4.4%
73
 
2.2%
53
 
2.2%
42
 
1.5%
82
 
1.5%
32
 
1.5%
Lowercase Letter
ValueCountFrequency (%)
n126
66.7%
a63
33.3%
Other Punctuation
ValueCountFrequency (%)
:34
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin189
52.6%
Common170
47.4%

Most frequent character per script

Common
ValueCountFrequency (%)
091
53.5%
:34
 
20.0%
220
 
11.8%
17
 
4.1%
66
 
3.5%
73
 
1.8%
53
 
1.8%
42
 
1.2%
82
 
1.2%
32
 
1.2%
Latin
ValueCountFrequency (%)
n126
66.7%
a63
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII359
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n126
35.1%
091
25.3%
a63
17.5%
:34
 
9.5%
220
 
5.6%
17
 
1.9%
66
 
1.7%
73
 
0.8%
53
 
0.8%
42
 
0.6%
Other values (2)4
 
1.1%

airstamp
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct20
Distinct (%)20.6%
Missing0
Missing (%)0.0%
Memory size904.0 B
2020-12-01T12:00:00+00:00
63 
2020-12-01T05:00:00+00:00
 
5
2020-12-01T04:00:00+00:00
 
4
2020-12-01T17:00:00+00:00
 
4
2020-12-01T09:00:00+00:00
 
2
Other values (15)
19 

Length

Max length25
Median length25
Mean length25
Min length25

Characters and Unicode

Total characters2425
Distinct characters13
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)11.3%

Sample

1st row2020-11-30T21:00:00+00:00
2nd row2020-12-01T00:00:00+00:00
3rd row2020-12-01T00:00:00+00:00
4th row2020-12-01T02:00:00+00:00
5th row2020-12-01T02:00:00+00:00

Common Values

ValueCountFrequency (%)
2020-12-01T12:00:00+00:0063
64.9%
2020-12-01T05:00:00+00:005
 
5.2%
2020-12-01T04:00:00+00:004
 
4.1%
2020-12-01T17:00:00+00:004
 
4.1%
2020-12-01T09:00:00+00:002
 
2.1%
2020-12-01T02:00:00+00:002
 
2.1%
2020-12-01T19:40:00+00:002
 
2.1%
2020-12-01T00:00:00+00:002
 
2.1%
2020-12-01T11:00:00+00:002
 
2.1%
2020-12-01T13:00:00+00:001
 
1.0%
Other values (10)10
 
10.3%

Length

2022-05-09T20:59:32.502046image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2020-12-01t12:00:00+00:0063
64.9%
2020-12-01t05:00:00+00:005
 
5.2%
2020-12-01t04:00:00+00:004
 
4.1%
2020-12-01t17:00:00+00:004
 
4.1%
2020-12-01t09:00:00+00:002
 
2.1%
2020-12-01t02:00:00+00:002
 
2.1%
2020-12-01t19:40:00+00:002
 
2.1%
2020-12-01t00:00:00+00:002
 
2.1%
2020-12-01t11:00:00+00:002
 
2.1%
2020-12-01t07:00:00+00:001
 
1.0%
Other values (10)10
 
10.3%

Most occurring characters

ValueCountFrequency (%)
01082
44.6%
2358
 
14.8%
:291
 
12.0%
1272
 
11.2%
-194
 
8.0%
T97
 
4.0%
+97
 
4.0%
59
 
0.4%
47
 
0.3%
37
 
0.3%
Other values (3)11
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1746
72.0%
Other Punctuation291
 
12.0%
Dash Punctuation194
 
8.0%
Uppercase Letter97
 
4.0%
Math Symbol97
 
4.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01082
62.0%
2358
 
20.5%
1272
 
15.6%
59
 
0.5%
47
 
0.4%
37
 
0.4%
75
 
0.3%
95
 
0.3%
61
 
0.1%
Other Punctuation
ValueCountFrequency (%)
:291
100.0%
Dash Punctuation
ValueCountFrequency (%)
-194
100.0%
Uppercase Letter
ValueCountFrequency (%)
T97
100.0%
Math Symbol
ValueCountFrequency (%)
+97
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2328
96.0%
Latin97
 
4.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01082
46.5%
2358
 
15.4%
:291
 
12.5%
1272
 
11.7%
-194
 
8.3%
+97
 
4.2%
59
 
0.4%
47
 
0.3%
37
 
0.3%
75
 
0.2%
Other values (2)6
 
0.3%
Latin
ValueCountFrequency (%)
T97
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2425
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01082
44.6%
2358
 
14.8%
:291
 
12.0%
1272
 
11.2%
-194
 
8.0%
T97
 
4.0%
+97
 
4.0%
59
 
0.4%
47
 
0.3%
37
 
0.3%
Other values (3)11
 
0.5%

runtime
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct35
Distinct (%)38.0%
Missing5
Missing (%)5.2%
Infinite0
Infinite (%)0.0%
Mean31.97826087
Minimum5
Maximum120
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size904.0 B
2022-05-09T20:59:32.592415image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile7.55
Q113
median25.5
Q345
95-th percentile73.5
Maximum120
Range115
Interquartile range (IQR)32

Descriptive statistics

Standard deviation23.05367757
Coefficient of variation (CV)0.7209171777
Kurtosis3.842036567
Mean31.97826087
Median Absolute Deviation (MAD)13.5
Skewness1.683990037
Sum2942
Variance531.4720497
MonotonicityNot monotonic
2022-05-09T20:59:32.728541image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
4515
15.5%
1311
 
11.3%
125
 
5.2%
144
 
4.1%
234
 
4.1%
304
 
4.1%
153
 
3.1%
373
 
3.1%
603
 
3.1%
253
 
3.1%
Other values (25)37
38.1%
(Missing)5
 
5.2%
ValueCountFrequency (%)
53
 
3.1%
61
 
1.0%
71
 
1.0%
81
 
1.0%
91
 
1.0%
101
 
1.0%
125
5.2%
1311
11.3%
144
 
4.1%
153
 
3.1%
ValueCountFrequency (%)
1202
 
2.1%
941
 
1.0%
902
 
2.1%
603
 
3.1%
571
 
1.0%
511
 
1.0%
503
 
3.1%
4515
15.5%
441
 
1.0%
411
 
1.0%

image
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct26
Distinct (%)100.0%
Missing71
Missing (%)73.2%
Memory size904.0 B
{'medium': 'https://static.tvmaze.com/uploads/images/medium_landscape/358/896916.jpg', 'original': 'https://static.tvmaze.com/uploads/images/original_untouched/358/896916.jpg'}
 
1
{'medium': 'https://static.tvmaze.com/uploads/images/medium_landscape/289/724604.jpg', 'original': 'https://static.tvmaze.com/uploads/images/original_untouched/289/724604.jpg'}
 
1
{'medium': 'https://static.tvmaze.com/uploads/images/medium_landscape/289/724603.jpg', 'original': 'https://static.tvmaze.com/uploads/images/original_untouched/289/724603.jpg'}
 
1
{'medium': 'https://static.tvmaze.com/uploads/images/medium_landscape/285/712958.jpg', 'original': 'https://static.tvmaze.com/uploads/images/original_untouched/285/712958.jpg'}
 
1
{'medium': 'https://static.tvmaze.com/uploads/images/medium_landscape/359/897951.jpg', 'original': 'https://static.tvmaze.com/uploads/images/original_untouched/359/897951.jpg'}
 
1
Other values (21)
21 

Length

Max length178
Median length176
Mean length176.1538462
Min length176

Characters and Unicode

Total characters4580
Distinct characters38
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)100.0%

Sample

1st row{'medium': 'https://static.tvmaze.com/uploads/images/medium_landscape/294/737206.jpg', 'original': 'https://static.tvmaze.com/uploads/images/original_untouched/294/737206.jpg'}
2nd row{'medium': 'https://static.tvmaze.com/uploads/images/medium_landscape/286/715105.jpg', 'original': 'https://static.tvmaze.com/uploads/images/original_untouched/286/715105.jpg'}
3rd row{'medium': 'https://static.tvmaze.com/uploads/images/medium_landscape/284/710997.jpg', 'original': 'https://static.tvmaze.com/uploads/images/original_untouched/284/710997.jpg'}
4th row{'medium': 'https://static.tvmaze.com/uploads/images/medium_landscape/284/710998.jpg', 'original': 'https://static.tvmaze.com/uploads/images/original_untouched/284/710998.jpg'}
5th row{'medium': 'https://static.tvmaze.com/uploads/images/medium_landscape/284/710999.jpg', 'original': 'https://static.tvmaze.com/uploads/images/original_untouched/284/710999.jpg'}

Common Values

ValueCountFrequency (%)
{'medium': 'https://static.tvmaze.com/uploads/images/medium_landscape/358/896916.jpg', 'original': 'https://static.tvmaze.com/uploads/images/original_untouched/358/896916.jpg'}1
 
1.0%
{'medium': 'https://static.tvmaze.com/uploads/images/medium_landscape/289/724604.jpg', 'original': 'https://static.tvmaze.com/uploads/images/original_untouched/289/724604.jpg'}1
 
1.0%
{'medium': 'https://static.tvmaze.com/uploads/images/medium_landscape/289/724603.jpg', 'original': 'https://static.tvmaze.com/uploads/images/original_untouched/289/724603.jpg'}1
 
1.0%
{'medium': 'https://static.tvmaze.com/uploads/images/medium_landscape/285/712958.jpg', 'original': 'https://static.tvmaze.com/uploads/images/original_untouched/285/712958.jpg'}1
 
1.0%
{'medium': 'https://static.tvmaze.com/uploads/images/medium_landscape/359/897951.jpg', 'original': 'https://static.tvmaze.com/uploads/images/original_untouched/359/897951.jpg'}1
 
1.0%
{'medium': 'https://static.tvmaze.com/uploads/images/medium_landscape/284/710176.jpg', 'original': 'https://static.tvmaze.com/uploads/images/original_untouched/284/710176.jpg'}1
 
1.0%
{'medium': 'https://static.tvmaze.com/uploads/images/medium_landscape/403/1009865.jpg', 'original': 'https://static.tvmaze.com/uploads/images/original_untouched/403/1009865.jpg'}1
 
1.0%
{'medium': 'https://static.tvmaze.com/uploads/images/medium_landscape/404/1012046.jpg', 'original': 'https://static.tvmaze.com/uploads/images/original_untouched/404/1012046.jpg'}1
 
1.0%
{'medium': 'https://static.tvmaze.com/uploads/images/medium_landscape/317/794449.jpg', 'original': 'https://static.tvmaze.com/uploads/images/original_untouched/317/794449.jpg'}1
 
1.0%
{'medium': 'https://static.tvmaze.com/uploads/images/medium_landscape/317/794448.jpg', 'original': 'https://static.tvmaze.com/uploads/images/original_untouched/317/794448.jpg'}1
 
1.0%
Other values (16)16
 
16.5%
(Missing)71
73.2%

Length

2022-05-09T20:59:32.861395image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
medium26
25.0%
original26
25.0%
https://static.tvmaze.com/uploads/images/original_untouched/285/714313.jpg1
 
1.0%
https://static.tvmaze.com/uploads/images/medium_landscape/286/715076.jpg1
 
1.0%
https://static.tvmaze.com/uploads/images/original_untouched/286/715076.jpg1
 
1.0%
https://static.tvmaze.com/uploads/images/medium_landscape/286/715059.jpg1
 
1.0%
https://static.tvmaze.com/uploads/images/original_untouched/286/715059.jpg1
 
1.0%
https://static.tvmaze.com/uploads/images/medium_landscape/285/714985.jpg1
 
1.0%
https://static.tvmaze.com/uploads/images/original_untouched/285/714985.jpg1
 
1.0%
https://static.tvmaze.com/uploads/images/medium_landscape/287/719812.jpg1
 
1.0%
Other values (44)44
42.3%

Most occurring characters

ValueCountFrequency (%)
/364
 
7.9%
a312
 
6.8%
t286
 
6.2%
m260
 
5.7%
i260
 
5.7%
s234
 
5.1%
e208
 
4.5%
'208
 
4.5%
o182
 
4.0%
p182
 
4.0%
Other values (28)2084
45.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter3068
67.0%
Other Punctuation858
 
18.7%
Decimal Number472
 
10.3%
Space Separator78
 
1.7%
Connector Punctuation52
 
1.1%
Close Punctuation26
 
0.6%
Open Punctuation26
 
0.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a312
 
10.2%
t286
 
9.3%
m260
 
8.5%
i260
 
8.5%
s234
 
7.6%
e208
 
6.8%
o182
 
5.9%
p182
 
5.9%
g156
 
5.1%
c156
 
5.1%
Other values (9)832
27.1%
Decimal Number
ValueCountFrequency (%)
770
14.8%
956
11.9%
154
11.4%
852
11.0%
452
11.0%
250
10.6%
040
8.5%
636
7.6%
534
7.2%
328
 
5.9%
Other Punctuation
ValueCountFrequency (%)
/364
42.4%
'208
24.2%
.156
18.2%
:104
 
12.1%
,26
 
3.0%
Space Separator
ValueCountFrequency (%)
78
100.0%
Connector Punctuation
ValueCountFrequency (%)
_52
100.0%
Close Punctuation
ValueCountFrequency (%)
}26
100.0%
Open Punctuation
ValueCountFrequency (%)
{26
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin3068
67.0%
Common1512
33.0%

Most frequent character per script

Common
ValueCountFrequency (%)
/364
24.1%
'208
13.8%
.156
10.3%
:104
 
6.9%
78
 
5.2%
770
 
4.6%
956
 
3.7%
154
 
3.6%
852
 
3.4%
_52
 
3.4%
Other values (9)318
21.0%
Latin
ValueCountFrequency (%)
a312
 
10.2%
t286
 
9.3%
m260
 
8.5%
i260
 
8.5%
s234
 
7.6%
e208
 
6.8%
o182
 
5.9%
p182
 
5.9%
g156
 
5.1%
c156
 
5.1%
Other values (9)832
27.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII4580
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/364
 
7.9%
a312
 
6.8%
t286
 
6.2%
m260
 
5.7%
i260
 
5.7%
s234
 
5.1%
e208
 
4.5%
'208
 
4.5%
o182
 
4.0%
p182
 
4.0%
Other values (28)2084
45.5%

summary
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct21
Distinct (%)21.6%
Missing0
Missing (%)0.0%
Memory size904.0 B
nan
77 
<p><b>#ObtainedAConversationalSkill #WeSetUpATent♥ #ManySmiles</b></p>
 
1
<p>Amie and Caolan had different ideas for their big day. She wanted a small wedding abroad, but he persuaded her to have a large and extravagant celebration.</p>
 
1
<p>Cheyenne se fait passer pour le bras droit de l'Anglais auprès d'un passeur nigérian.</p>
 
1
<p>Cheyenne découvre qu'un policier ripou dont elle ignore l'identité est l'informateur de Yannick.</p>
 
1
Other values (16)
16 

Length

Max length1077
Median length3
Mean length45.46391753
Min length3

Characters and Unicode

Total characters4410
Distinct characters79
Distinct categories11 ?
Distinct scripts2 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20 ?
Unique (%)20.6%

Sample

1st row<p><b>#ObtainedAConversationalSkill #WeSetUpATent♥ #ManySmiles</b></p>
2nd rownan
3rd rownan
4th rownan
5th rownan

Common Values

ValueCountFrequency (%)
nan77
79.4%
<p><b>#ObtainedAConversationalSkill #WeSetUpATent♥ #ManySmiles</b></p>1
 
1.0%
<p>Amie and Caolan had different ideas for their big day. She wanted a small wedding abroad, but he persuaded her to have a large and extravagant celebration.</p>1
 
1.0%
<p>Cheyenne se fait passer pour le bras droit de l'Anglais auprès d'un passeur nigérian.</p>1
 
1.0%
<p>Cheyenne découvre qu'un policier ripou dont elle ignore l'identité est l'informateur de Yannick.</p>1
 
1.0%
<p>K2 is a mysterious and extremly destructive drug that 18 yr old Yazz not only abused but also trafficked, earning him 100k a week while also causing him to spiral out of control.</p>1
 
1.0%
<p>Inside The Hill breaks down Trump's voter fraud conspiracy theories, Biden's cabinet picks, and Senate candidate Kelly Loeffler's campaign ad with guest Rep. Linda Sanchez (D-CA).</p>1
 
1.0%
<p>As Claire's world begins to crumble, an uncertain Eric is pressed to reveal intimate details of his relationship.</p>1
 
1.0%
<p>After the audition, 'Yuki Ebana' was confessed to 'Seiichi Izumi'. 'Shiori Kato' and 'Hitoko Murakami' witnessed it, and Hitoko, who had been thinking about Seiichi, screamed and ran away, and Shiori chased it.<br />On the other hand, for some reason, 'Ryo' will be drunk with Seiichi's band members 'Taki Hiroto' and 'Tsubaki Aoi', and will be re-drinked at Taki and Taki's house. Taki tells Ryo that he is thinking of leaving the band and becoming a doctor. Ryo presses the taiko stamp that Taki can be compatible, but Taki is not serious. Taki notices that something is wrong with Ryo's body, but Ryo tells others to keep silent.<br />In Seiichi's confession, Yuki and Hitoko were jerky. Meanwhile, the president 'Yuichi Yanagishita' called and told that the major debut of 'Chahhan' was decided. Three people who rejoice.<br />However, when I went to the office a few days later, I found that the debut song was not the original song, but a song made by a stranger ('77'). The three are not convinced, but Yuki remembers something stuck in the lyrics of 'No. 77' ...</p>1
 
1.0%
<p>Kiki is flustered to learn that Takumi in fact heard her confess her feelings, and now he has something he really wants to tell her. Meanwhile at work, the new "mobile convenience store" project is launched. When Kiki sees Takumi and Riho together, however, she decides it's best to forget about him. But Makoto's own feelings for her haven't changed, and he decides it's time to take their relationship to the next level.</p>1
 
1.0%
Other values (11)11
 
11.3%

Length

2022-05-09T20:59:33.046530image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nan77
 
10.3%
the28
 
3.7%
and27
 
3.6%
to21
 
2.8%
a14
 
1.9%
of11
 
1.5%
but10
 
1.3%
that8
 
1.1%
is8
 
1.1%
their8
 
1.1%
Other values (405)537
71.7%

Most occurring characters

ValueCountFrequency (%)
651
14.8%
e378
 
8.6%
n377
 
8.5%
a329
 
7.5%
i265
 
6.0%
t257
 
5.8%
o214
 
4.9%
s183
 
4.1%
r172
 
3.9%
h166
 
3.8%
Other values (69)1418
32.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter3326
75.4%
Space Separator654
 
14.8%
Other Punctuation165
 
3.7%
Uppercase Letter132
 
3.0%
Math Symbol90
 
2.0%
Decimal Number25
 
0.6%
Dash Punctuation8
 
0.2%
Close Punctuation3
 
0.1%
Currency Symbol3
 
0.1%
Open Punctuation3
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e378
11.4%
n377
11.3%
a329
 
9.9%
i265
 
8.0%
t257
 
7.7%
o214
 
6.4%
s183
 
5.5%
r172
 
5.2%
h166
 
5.0%
d149
 
4.5%
Other values (18)836
25.1%
Uppercase Letter
ValueCountFrequency (%)
T20
15.2%
S13
 
9.8%
A10
 
7.6%
C9
 
6.8%
D8
 
6.1%
H8
 
6.1%
M8
 
6.1%
R7
 
5.3%
Y7
 
5.3%
I6
 
4.5%
Other values (13)36
27.3%
Other Punctuation
ValueCountFrequency (%)
,48
29.1%
'44
26.7%
.39
23.6%
/24
14.5%
#3
 
1.8%
?2
 
1.2%
"2
 
1.2%
!1
 
0.6%
;1
 
0.6%
&1
 
0.6%
Decimal Number
ValueCountFrequency (%)
012
48.0%
74
 
16.0%
13
 
12.0%
62
 
8.0%
31
 
4.0%
51
 
4.0%
81
 
4.0%
21
 
4.0%
Space Separator
ValueCountFrequency (%)
651
99.5%
 3
 
0.5%
Math Symbol
ValueCountFrequency (%)
>45
50.0%
<45
50.0%
Dash Punctuation
ValueCountFrequency (%)
-6
75.0%
2
 
25.0%
Close Punctuation
ValueCountFrequency (%)
)3
100.0%
Currency Symbol
ValueCountFrequency (%)
£3
100.0%
Open Punctuation
ValueCountFrequency (%)
(3
100.0%
Other Symbol
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin3458
78.4%
Common952
 
21.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e378
 
10.9%
n377
 
10.9%
a329
 
9.5%
i265
 
7.7%
t257
 
7.4%
o214
 
6.2%
s183
 
5.3%
r172
 
5.0%
h166
 
4.8%
d149
 
4.3%
Other values (41)968
28.0%
Common
ValueCountFrequency (%)
651
68.4%
,48
 
5.0%
>45
 
4.7%
<45
 
4.7%
'44
 
4.6%
.39
 
4.1%
/24
 
2.5%
012
 
1.3%
-6
 
0.6%
74
 
0.4%
Other values (18)34
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII4397
99.7%
None10
 
0.2%
Punctuation2
 
< 0.1%
Misc Symbols1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
651
14.8%
e378
 
8.6%
n377
 
8.6%
a329
 
7.5%
i265
 
6.0%
t257
 
5.8%
o214
 
4.9%
s183
 
4.2%
r172
 
3.9%
h166
 
3.8%
Other values (63)1405
32.0%
None
ValueCountFrequency (%)
£3
30.0%
é3
30.0%
 3
30.0%
è1
 
10.0%
Punctuation
ValueCountFrequency (%)
2
100.0%
Misc Symbols
ValueCountFrequency (%)
1
100.0%

_embedded_show_id
Real number (ℝ≥0)

HIGH CORRELATION

Distinct67
Distinct (%)69.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47701.64948
Minimum2504
Maximum61755
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size904.0 B
2022-05-09T20:59:33.242240image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2504
5-th percentile17156.4
Q145812
median52038
Q353282
95-th percentile57589.8
Maximum61755
Range59251
Interquartile range (IQR)7470

Descriptive statistics

Standard deviation11516.54299
Coefficient of variation (CV)0.2414286113
Kurtosis5.018726898
Mean47701.64948
Median Absolute Deviation (MAD)1377
Skewness-2.268958905
Sum4627060
Variance132630762.5
MonotonicityNot monotonic
2022-05-09T20:59:33.422894image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5328212
 
12.4%
453896
 
6.2%
553644
 
4.1%
518933
 
3.1%
521062
 
2.1%
521642
 
2.1%
521592
 
2.1%
521082
 
2.1%
521072
 
2.1%
521042
 
2.1%
Other values (57)60
61.9%
ValueCountFrequency (%)
25041
1.0%
64411
1.0%
133811
1.0%
133921
1.0%
152501
1.0%
176331
1.0%
214911
1.0%
262681
1.0%
306061
1.0%
320871
1.0%
ValueCountFrequency (%)
617551
 
1.0%
616741
 
1.0%
615301
 
1.0%
607851
 
1.0%
585931
 
1.0%
573391
 
1.0%
553644
4.1%
550161
 
1.0%
541121
 
1.0%
540331
 
1.0%

_embedded_show_url
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct67
Distinct (%)69.1%
Missing0
Missing (%)0.0%
Memory size904.0 B
https://www.tvmaze.com/shows/53282/33-rebenka
12 
https://www.tvmaze.com/shows/45389/obsolete
 
6
https://www.tvmaze.com/shows/55364/countdown-to-i-do
 
4
https://www.tvmaze.com/shows/51893/exit-nordpolen
 
3
https://www.tvmaze.com/shows/52106/insect-detective
 
2
Other values (62)
70 

Length

Max length71
Median length58
Mean length49.51546392
Min length40

Characters and Unicode

Total characters4803
Distinct characters39
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique54 ?
Unique (%)55.7%

Sample

1st rowhttps://www.tvmaze.com/shows/41648/sim-for-you
2nd rowhttps://www.tvmaze.com/shows/52198/kotiki
3rd rowhttps://www.tvmaze.com/shows/52933/lab-s-antonom-belaevym
4th rowhttps://www.tvmaze.com/shows/51336/core-sense
5th rowhttps://www.tvmaze.com/shows/54033/wu-shen-zhu-zai

Common Values

ValueCountFrequency (%)
https://www.tvmaze.com/shows/53282/33-rebenka12
 
12.4%
https://www.tvmaze.com/shows/45389/obsolete6
 
6.2%
https://www.tvmaze.com/shows/55364/countdown-to-i-do4
 
4.1%
https://www.tvmaze.com/shows/51893/exit-nordpolen3
 
3.1%
https://www.tvmaze.com/shows/52106/insect-detective2
 
2.1%
https://www.tvmaze.com/shows/52164/the-holiday-movies-that-made-us2
 
2.1%
https://www.tvmaze.com/shows/52159/to-love2
 
2.1%
https://www.tvmaze.com/shows/52108/psych-hunter2
 
2.1%
https://www.tvmaze.com/shows/52107/new-face2
 
2.1%
https://www.tvmaze.com/shows/52104/twisted-fate-of-love2
 
2.1%
Other values (57)60
61.9%

Length

2022-05-09T20:59:33.580317image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://www.tvmaze.com/shows/53282/33-rebenka12
 
12.4%
https://www.tvmaze.com/shows/45389/obsolete6
 
6.2%
https://www.tvmaze.com/shows/55364/countdown-to-i-do4
 
4.1%
https://www.tvmaze.com/shows/51893/exit-nordpolen3
 
3.1%
https://www.tvmaze.com/shows/52107/new-face2
 
2.1%
https://www.tvmaze.com/shows/51870/something-just-like-this2
 
2.1%
https://www.tvmaze.com/shows/52038/please-wait-brother2
 
2.1%
https://www.tvmaze.com/shows/52104/twisted-fate-of-love2
 
2.1%
https://www.tvmaze.com/shows/50106/cheyenne-et-lola2
 
2.1%
https://www.tvmaze.com/shows/52108/psych-hunter2
 
2.1%
Other values (57)60
61.9%

Most occurring characters

ValueCountFrequency (%)
/485
 
10.1%
w411
 
8.6%
t391
 
8.1%
s368
 
7.7%
o302
 
6.3%
e262
 
5.5%
h238
 
5.0%
m226
 
4.7%
a202
 
4.2%
.194
 
4.0%
Other values (29)1724
35.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter3364
70.0%
Other Punctuation776
 
16.2%
Decimal Number509
 
10.6%
Dash Punctuation154
 
3.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
w411
12.2%
t391
11.6%
s368
10.9%
o302
9.0%
e262
 
7.8%
h238
 
7.1%
m226
 
6.7%
a202
 
6.0%
c125
 
3.7%
v118
 
3.5%
Other values (15)721
21.4%
Decimal Number
ValueCountFrequency (%)
588
17.3%
387
17.1%
266
13.0%
162
12.2%
445
8.8%
844
8.6%
037
7.3%
635
 
6.9%
927
 
5.3%
718
 
3.5%
Other Punctuation
ValueCountFrequency (%)
/485
62.5%
.194
 
25.0%
:97
 
12.5%
Dash Punctuation
ValueCountFrequency (%)
-154
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin3364
70.0%
Common1439
30.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
w411
12.2%
t391
11.6%
s368
10.9%
o302
9.0%
e262
 
7.8%
h238
 
7.1%
m226
 
6.7%
a202
 
6.0%
c125
 
3.7%
v118
 
3.5%
Other values (15)721
21.4%
Common
ValueCountFrequency (%)
/485
33.7%
.194
 
13.5%
-154
 
10.7%
:97
 
6.7%
588
 
6.1%
387
 
6.0%
266
 
4.6%
162
 
4.3%
445
 
3.1%
844
 
3.1%
Other values (4)117
 
8.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII4803
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/485
 
10.1%
w411
 
8.6%
t391
 
8.1%
s368
 
7.7%
o302
 
6.3%
e262
 
5.5%
h238
 
5.0%
m226
 
4.7%
a202
 
4.2%
.194
 
4.0%
Other values (29)1724
35.9%

_embedded_show_name
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct67
Distinct (%)69.1%
Missing0
Missing (%)0.0%
Memory size904.0 B
33 Ребёнка
12 
Obsolete
 
6
Countdown to I Do
 
4
Exit Nordpolen
 
3
Insect Detective
 
2
Other values (62)
70 

Length

Max length36
Median length26
Mean length14.70103093
Min length5

Characters and Unicode

Total characters1426
Distinct characters93
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique54 ?
Unique (%)55.7%

Sample

1st rowSim for You
2nd rowКотики
3rd rowLAB с Антоном Беляевым
4th rowCore Sense
5th rowWu Shen Zhu Zai

Common Values

ValueCountFrequency (%)
33 Ребёнка12
 
12.4%
Obsolete6
 
6.2%
Countdown to I Do4
 
4.1%
Exit Nordpolen3
 
3.1%
Insect Detective2
 
2.1%
The Holiday Movies That Made Us2
 
2.1%
To Love2
 
2.1%
Psych Hunter2
 
2.1%
New Face2
 
2.1%
Twisted Fate of Love2
 
2.1%
Other values (57)60
61.9%

Length

2022-05-09T20:59:33.735964image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
3312
 
4.7%
ребёнка12
 
4.7%
obsolete6
 
2.4%
to6
 
2.4%
the5
 
2.0%
i5
 
2.0%
of4
 
1.6%
love4
 
1.6%
countdown4
 
1.6%
a4
 
1.6%
Other values (153)191
75.5%

Most occurring characters

ValueCountFrequency (%)
156
 
10.9%
e126
 
8.8%
o90
 
6.3%
a75
 
5.3%
t71
 
5.0%
n61
 
4.3%
i60
 
4.2%
s57
 
4.0%
r46
 
3.2%
l45
 
3.2%
Other values (83)639
44.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1003
70.3%
Uppercase Letter228
 
16.0%
Space Separator156
 
10.9%
Decimal Number26
 
1.8%
Other Punctuation12
 
0.8%
Dash Punctuation1
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e126
 
12.6%
o90
 
9.0%
a75
 
7.5%
t71
 
7.1%
n61
 
6.1%
i60
 
6.0%
s57
 
5.7%
r46
 
4.6%
l45
 
4.5%
h35
 
3.5%
Other values (42)337
33.6%
Uppercase Letter
ValueCountFrequency (%)
T24
 
10.5%
S15
 
6.6%
L14
 
6.1%
A13
 
5.7%
Р13
 
5.7%
M13
 
5.7%
F11
 
4.8%
C11
 
4.8%
N11
 
4.8%
I11
 
4.8%
Other values (21)92
40.4%
Other Punctuation
ValueCountFrequency (%)
.5
41.7%
,3
25.0%
'2
 
16.7%
:1
 
8.3%
&1
 
8.3%
Decimal Number
ValueCountFrequency (%)
324
92.3%
01
 
3.8%
11
 
3.8%
Space Separator
ValueCountFrequency (%)
156
100.0%
Dash Punctuation
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1083
75.9%
Common195
 
13.7%
Cyrillic148
 
10.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e126
 
11.6%
o90
 
8.3%
a75
 
6.9%
t71
 
6.6%
n61
 
5.6%
i60
 
5.5%
s57
 
5.3%
r46
 
4.2%
l45
 
4.2%
h35
 
3.2%
Other values (43)417
38.5%
Cyrillic
ValueCountFrequency (%)
е19
12.8%
н15
10.1%
Р13
 
8.8%
а13
 
8.8%
к13
 
8.8%
б12
 
8.1%
ё12
 
8.1%
о7
 
4.7%
в5
 
3.4%
т5
 
3.4%
Other values (20)34
23.0%
Common
ValueCountFrequency (%)
156
80.0%
324
 
12.3%
.5
 
2.6%
,3
 
1.5%
'2
 
1.0%
01
 
0.5%
11
 
0.5%
1
 
0.5%
:1
 
0.5%
&1
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII1271
89.1%
Cyrillic148
 
10.4%
None6
 
0.4%
Punctuation1
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
156
 
12.3%
e126
 
9.9%
o90
 
7.1%
a75
 
5.9%
t71
 
5.6%
n61
 
4.8%
i60
 
4.7%
s57
 
4.5%
r46
 
3.6%
l45
 
3.5%
Other values (48)484
38.1%
Cyrillic
ValueCountFrequency (%)
е19
12.8%
н15
10.1%
Р13
 
8.8%
а13
 
8.8%
к13
 
8.8%
б12
 
8.1%
ё12
 
8.1%
о7
 
4.7%
в5
 
3.4%
т5
 
3.4%
Other values (20)34
23.0%
None
ValueCountFrequency (%)
ø3
50.0%
ı1
 
16.7%
á1
 
16.7%
ş1
 
16.7%
Punctuation
ValueCountFrequency (%)
1
100.0%

_embedded_show_type
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct8
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Memory size904.0 B
Scripted
48 
Documentary
12 
Animation
11 
Reality
Talk Show
Other values (3)
10 

Length

Max length11
Median length9
Mean length8.257731959
Min length4

Characters and Unicode

Total characters801
Distinct characters26
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowReality
2nd rowScripted
3rd rowDocumentary
4th rowAnimation
5th rowAnimation

Common Values

ValueCountFrequency (%)
Scripted48
49.5%
Documentary12
 
12.4%
Animation11
 
11.3%
Reality9
 
9.3%
Talk Show7
 
7.2%
Variety4
 
4.1%
Sports4
 
4.1%
News2
 
2.1%

Length

2022-05-09T20:59:33.894342image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T20:59:34.011631image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
scripted48
46.2%
documentary12
 
11.5%
animation11
 
10.6%
reality9
 
8.7%
talk7
 
6.7%
show7
 
6.7%
variety4
 
3.8%
sports4
 
3.8%
news2
 
1.9%

Most occurring characters

ValueCountFrequency (%)
t88
11.0%
i83
10.4%
e75
 
9.4%
r68
 
8.5%
c60
 
7.5%
S59
 
7.4%
p52
 
6.5%
d48
 
6.0%
a43
 
5.4%
o34
 
4.2%
Other values (16)191
23.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter690
86.1%
Uppercase Letter104
 
13.0%
Space Separator7
 
0.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t88
12.8%
i83
12.0%
e75
10.9%
r68
9.9%
c60
8.7%
p52
7.5%
d48
7.0%
a43
6.2%
o34
 
4.9%
n34
 
4.9%
Other values (8)105
15.2%
Uppercase Letter
ValueCountFrequency (%)
S59
56.7%
D12
 
11.5%
A11
 
10.6%
R9
 
8.7%
T7
 
6.7%
V4
 
3.8%
N2
 
1.9%
Space Separator
ValueCountFrequency (%)
7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin794
99.1%
Common7
 
0.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
t88
11.1%
i83
10.5%
e75
9.4%
r68
 
8.6%
c60
 
7.6%
S59
 
7.4%
p52
 
6.5%
d48
 
6.0%
a43
 
5.4%
o34
 
4.3%
Other values (15)184
23.2%
Common
ValueCountFrequency (%)
7
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII801
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t88
11.0%
i83
10.4%
e75
 
9.4%
r68
 
8.5%
c60
 
7.5%
S59
 
7.4%
p52
 
6.5%
d48
 
6.0%
a43
 
5.4%
o34
 
4.2%
Other values (16)191
23.8%

_embedded_show_language
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct14
Distinct (%)14.4%
Missing0
Missing (%)0.0%
Memory size904.0 B
English
28 
Russian
21 
Chinese
18 
Japanese
Norwegian
Other values (9)
15 

Length

Max length9
Median length7
Mean length7.12371134
Min length5

Characters and Unicode

Total characters691
Distinct characters30
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)4.1%

Sample

1st rowKorean
2nd rowRussian
3rd rowRussian
4th rowChinese
5th rowChinese

Common Values

ValueCountFrequency (%)
English28
28.9%
Russian21
21.6%
Chinese18
18.6%
Japanese9
 
9.3%
Norwegian6
 
6.2%
Arabic3
 
3.1%
Korean2
 
2.1%
Turkish2
 
2.1%
Spanish2
 
2.1%
French2
 
2.1%
Other values (4)4
 
4.1%

Length

2022-05-09T20:59:34.172114image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
english28
28.9%
russian21
21.6%
chinese18
18.6%
japanese9
 
9.3%
norwegian6
 
6.2%
arabic3
 
3.1%
korean2
 
2.1%
turkish2
 
2.1%
spanish2
 
2.1%
french2
 
2.1%
Other values (4)4
 
4.1%

Most occurring characters

ValueCountFrequency (%)
s103
14.9%
n89
12.9%
i82
11.9%
e66
9.6%
a55
8.0%
h54
7.8%
g36
 
5.2%
l29
 
4.2%
E28
 
4.1%
u24
 
3.5%
Other values (20)125
18.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter594
86.0%
Uppercase Letter97
 
14.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s103
17.3%
n89
15.0%
i82
13.8%
e66
11.1%
a55
9.3%
h54
9.1%
g36
 
6.1%
l29
 
4.9%
u24
 
4.0%
r16
 
2.7%
Other values (8)40
 
6.7%
Uppercase Letter
ValueCountFrequency (%)
E28
28.9%
R21
21.6%
C18
18.6%
J9
 
9.3%
N6
 
6.2%
A3
 
3.1%
T3
 
3.1%
S3
 
3.1%
K2
 
2.1%
F2
 
2.1%
Other values (2)2
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Latin691
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
s103
14.9%
n89
12.9%
i82
11.9%
e66
9.6%
a55
8.0%
h54
7.8%
g36
 
5.2%
l29
 
4.2%
E28
 
4.1%
u24
 
3.5%
Other values (20)125
18.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII691
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s103
14.9%
n89
12.9%
i82
11.9%
e66
9.6%
a55
8.0%
h54
7.8%
g36
 
5.2%
l29
 
4.2%
E28
 
4.1%
u24
 
3.5%
Other values (20)125
18.1%

_embedded_show_genres
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct32
Distinct (%)33.0%
Missing0
Missing (%)0.0%
Memory size904.0 B
[]
36 
['Comedy']
10 
['Drama', 'Science-Fiction']
['Drama', 'Thriller', 'Mystery']
['Drama', 'Romance']
 
3
Other values (27)
38 

Length

Max length43
Median length36
Mean length14.77319588
Min length2

Characters and Unicode

Total characters1433
Distinct characters35
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)19.6%

Sample

1st row[]
2nd row['Comedy']
3rd row['Music']
4th row['Action', 'Anime', 'Science-Fiction']
5th row['Action', 'Adventure', 'Anime', 'Fantasy']

Common Values

ValueCountFrequency (%)
[]36
37.1%
['Comedy']10
 
10.3%
['Drama', 'Science-Fiction']6
 
6.2%
['Drama', 'Thriller', 'Mystery']4
 
4.1%
['Drama', 'Romance']3
 
3.1%
['Romance']3
 
3.1%
['Drama', 'Romance', 'History']3
 
3.1%
['Adventure', 'Nature']3
 
3.1%
['Drama', 'Crime']2
 
2.1%
['Action', 'Crime', 'Thriller']2
 
2.1%
Other values (22)25
25.8%

Length

2022-05-09T20:59:34.278060image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
36
22.1%
drama28
17.2%
comedy17
10.4%
romance11
 
6.7%
crime10
 
6.1%
science-fiction8
 
4.9%
thriller8
 
4.9%
mystery7
 
4.3%
action5
 
3.1%
adventure5
 
3.1%
Other values (12)28
17.2%

Most occurring characters

ValueCountFrequency (%)
'254
17.7%
[97
 
6.8%
]97
 
6.8%
e93
 
6.5%
r81
 
5.7%
a80
 
5.6%
m70
 
4.9%
66
 
4.6%
,66
 
4.6%
i63
 
4.4%
Other values (25)466
32.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter710
49.5%
Other Punctuation320
22.3%
Uppercase Letter135
 
9.4%
Open Punctuation97
 
6.8%
Close Punctuation97
 
6.8%
Space Separator66
 
4.6%
Dash Punctuation8
 
0.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e93
13.1%
r81
11.4%
a80
11.3%
m70
9.9%
i63
8.9%
o50
7.0%
n47
6.6%
c45
6.3%
t37
 
5.2%
y37
 
5.2%
Other values (8)107
15.1%
Uppercase Letter
ValueCountFrequency (%)
C31
23.0%
D28
20.7%
F13
9.6%
A13
9.6%
M12
 
8.9%
S11
 
8.1%
R11
 
8.1%
T8
 
5.9%
N4
 
3.0%
H3
 
2.2%
Other Punctuation
ValueCountFrequency (%)
'254
79.4%
,66
 
20.6%
Open Punctuation
ValueCountFrequency (%)
[97
100.0%
Close Punctuation
ValueCountFrequency (%)
]97
100.0%
Space Separator
ValueCountFrequency (%)
66
100.0%
Dash Punctuation
ValueCountFrequency (%)
-8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin845
59.0%
Common588
41.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e93
 
11.0%
r81
 
9.6%
a80
 
9.5%
m70
 
8.3%
i63
 
7.5%
o50
 
5.9%
n47
 
5.6%
c45
 
5.3%
t37
 
4.4%
y37
 
4.4%
Other values (19)242
28.6%
Common
ValueCountFrequency (%)
'254
43.2%
[97
 
16.5%
]97
 
16.5%
66
 
11.2%
,66
 
11.2%
-8
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII1433
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
'254
17.7%
[97
 
6.8%
]97
 
6.8%
e93
 
6.5%
r81
 
5.7%
a80
 
5.6%
m70
 
4.9%
66
 
4.6%
,66
 
4.6%
i63
 
4.4%
Other values (25)466
32.5%

_embedded_show_status
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size904.0 B
Ended
43 
Running
40 
To Be Determined
14 

Length

Max length16
Median length7
Mean length7.412371134
Min length5

Characters and Unicode

Total characters719
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRunning
2nd rowEnded
3rd rowRunning
4th rowRunning
5th rowRunning

Common Values

ValueCountFrequency (%)
Ended43
44.3%
Running40
41.2%
To Be Determined14
 
14.4%

Length

2022-05-09T20:59:34.378898image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T20:59:34.508243image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
ended43
34.4%
running40
32.0%
to14
 
11.2%
be14
 
11.2%
determined14
 
11.2%

Most occurring characters

ValueCountFrequency (%)
n177
24.6%
d100
13.9%
e99
13.8%
i54
 
7.5%
E43
 
6.0%
R40
 
5.6%
u40
 
5.6%
g40
 
5.6%
28
 
3.9%
T14
 
1.9%
Other values (6)84
11.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter566
78.7%
Uppercase Letter125
 
17.4%
Space Separator28
 
3.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n177
31.3%
d100
17.7%
e99
17.5%
i54
 
9.5%
u40
 
7.1%
g40
 
7.1%
o14
 
2.5%
t14
 
2.5%
r14
 
2.5%
m14
 
2.5%
Uppercase Letter
ValueCountFrequency (%)
E43
34.4%
R40
32.0%
T14
 
11.2%
B14
 
11.2%
D14
 
11.2%
Space Separator
ValueCountFrequency (%)
28
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin691
96.1%
Common28
 
3.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
n177
25.6%
d100
14.5%
e99
14.3%
i54
 
7.8%
E43
 
6.2%
R40
 
5.8%
u40
 
5.8%
g40
 
5.8%
T14
 
2.0%
o14
 
2.0%
Other values (5)70
 
10.1%
Common
ValueCountFrequency (%)
28
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII719
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n177
24.6%
d100
13.9%
e99
13.8%
i54
 
7.5%
E43
 
6.0%
R40
 
5.6%
u40
 
5.6%
g40
 
5.6%
28
 
3.9%
T14
 
1.9%
Other values (6)84
11.7%

_embedded_show_runtime
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct24
Distinct (%)29.6%
Missing16
Missing (%)16.5%
Infinite0
Infinite (%)0.0%
Mean31.91358025
Minimum5
Maximum120
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size904.0 B
2022-05-09T20:59:34.594977image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile10
Q113
median25
Q345
95-th percentile60
Maximum120
Range115
Interquartile range (IQR)32

Descriptive statistics

Standard deviation22.79210254
Coefficient of variation (CV)0.7141819365
Kurtosis4.434290377
Mean31.91358025
Median Absolute Deviation (MAD)12
Skewness1.786042243
Sum2585
Variance519.4799383
MonotonicityNot monotonic
2022-05-09T20:59:34.716410image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
1318
18.6%
4514
14.4%
406
 
6.2%
235
 
5.2%
304
 
4.1%
254
 
4.1%
153
 
3.1%
503
 
3.1%
603
 
3.1%
52
 
2.1%
Other values (14)19
19.6%
(Missing)16
16.5%
ValueCountFrequency (%)
52
 
2.1%
61
 
1.0%
81
 
1.0%
101
 
1.0%
121
 
1.0%
1318
18.6%
141
 
1.0%
153
 
3.1%
161
 
1.0%
202
 
2.1%
ValueCountFrequency (%)
1202
 
2.1%
902
 
2.1%
603
 
3.1%
571
 
1.0%
503
 
3.1%
4514
14.4%
406
6.2%
372
 
2.1%
304
 
4.1%
272
 
2.1%

_embedded_show_averageRuntime
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct34
Distinct (%)36.2%
Missing3
Missing (%)3.1%
Infinite0
Infinite (%)0.0%
Mean31.07446809
Minimum5
Maximum120
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size904.0 B
2022-05-09T20:59:34.827608image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile8.65
Q113
median25
Q345
95-th percentile69.45
Maximum120
Range115
Interquartile range (IQR)32

Descriptive statistics

Standard deviation22.72426554
Coefficient of variation (CV)0.7312841358
Kurtosis4.133597843
Mean31.07446809
Median Absolute Deviation (MAD)12
Skewness1.762450708
Sum2921
Variance516.3922443
MonotonicityNot monotonic
2022-05-09T20:59:35.065342image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
1319
19.6%
4515
15.5%
256
 
6.2%
234
 
4.1%
144
 
4.1%
503
 
3.1%
603
 
3.1%
263
 
3.1%
363
 
3.1%
162
 
2.1%
Other values (24)32
33.0%
(Missing)3
 
3.1%
ValueCountFrequency (%)
52
 
2.1%
62
 
2.1%
81
 
1.0%
91
 
1.0%
101
 
1.0%
111
 
1.0%
122
 
2.1%
1319
19.6%
144
 
4.1%
151
 
1.0%
ValueCountFrequency (%)
1202
 
2.1%
902
 
2.1%
871
 
1.0%
603
 
3.1%
591
 
1.0%
503
 
3.1%
471
 
1.0%
4515
15.5%
421
 
1.0%
401
 
1.0%

_embedded_show_premiered
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct50
Distinct (%)51.5%
Missing0
Missing (%)0.0%
Memory size904.0 B
2020-12-01
24 
2019-12-03
2020-11-24
2020-10-20
 
5
2020-11-23
 
4
Other values (45)
52 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters970
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique39 ?
Unique (%)40.2%

Sample

1st row2019-03-25
2nd row2020-11-30
3rd row2019-12-17
4th row2020-10-13
5th row2020-03-08

Common Values

ValueCountFrequency (%)
2020-12-0124
24.7%
2019-12-036
 
6.2%
2020-11-246
 
6.2%
2020-10-205
 
5.2%
2020-11-234
 
4.1%
2020-11-173
 
3.1%
2020-11-192
 
2.1%
2020-10-132
 
2.1%
2020-11-302
 
2.1%
2020-11-082
 
2.1%
Other values (40)41
42.3%

Length

2022-05-09T20:59:35.171604image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2020-12-0124
24.7%
2020-11-246
 
6.2%
2019-12-036
 
6.2%
2020-10-205
 
5.2%
2020-11-234
 
4.1%
2020-11-173
 
3.1%
2020-11-302
 
2.1%
2020-11-032
 
2.1%
2020-11-082
 
2.1%
2020-10-132
 
2.1%
Other values (40)41
42.3%

Most occurring characters

ValueCountFrequency (%)
0251
25.9%
2226
23.3%
-194
20.0%
1183
18.9%
330
 
3.1%
929
 
3.0%
818
 
1.9%
414
 
1.4%
712
 
1.2%
510
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number776
80.0%
Dash Punctuation194
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0251
32.3%
2226
29.1%
1183
23.6%
330
 
3.9%
929
 
3.7%
818
 
2.3%
414
 
1.8%
712
 
1.5%
510
 
1.3%
63
 
0.4%
Dash Punctuation
ValueCountFrequency (%)
-194
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common970
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0251
25.9%
2226
23.3%
-194
20.0%
1183
18.9%
330
 
3.1%
929
 
3.0%
818
 
1.9%
414
 
1.4%
712
 
1.2%
510
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII970
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0251
25.9%
2226
23.3%
-194
20.0%
1183
18.9%
330
 
3.1%
929
 
3.0%
818
 
1.9%
414
 
1.4%
712
 
1.2%
510
 
1.0%

_embedded_show_ended
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct18
Distinct (%)18.6%
Missing0
Missing (%)0.0%
Memory size904.0 B
nan
54 
2020-12-01
18 
2020-12-22
 
3
2020-12-03
 
2
2020-12-23
 
2
Other values (13)
18 

Length

Max length10
Median length3
Mean length6.103092784
Min length3

Characters and Unicode

Total characters592
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)8.2%

Sample

1st rownan
2nd row2020-12-11
3rd rownan
4th rownan
5th rownan

Common Values

ValueCountFrequency (%)
nan54
55.7%
2020-12-0118
 
18.6%
2020-12-223
 
3.1%
2020-12-032
 
2.1%
2020-12-232
 
2.1%
2020-12-162
 
2.1%
2020-12-022
 
2.1%
2020-12-302
 
2.1%
2020-12-152
 
2.1%
2020-12-082
 
2.1%
Other values (8)8
 
8.2%

Length

2022-05-09T20:59:35.257784image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nan54
55.7%
2020-12-0118
 
18.6%
2020-12-223
 
3.1%
2020-12-022
 
2.1%
2020-12-152
 
2.1%
2020-12-302
 
2.1%
2020-12-082
 
2.1%
2020-12-162
 
2.1%
2020-12-232
 
2.1%
2020-12-032
 
2.1%
Other values (8)8
 
8.2%

Most occurring characters

ValueCountFrequency (%)
2140
23.6%
0113
19.1%
n108
18.2%
-86
14.5%
174
12.5%
a54
 
9.1%
36
 
1.0%
53
 
0.5%
83
 
0.5%
62
 
0.3%
Other values (2)3
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number344
58.1%
Lowercase Letter162
27.4%
Dash Punctuation86
 
14.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2140
40.7%
0113
32.8%
174
21.5%
36
 
1.7%
53
 
0.9%
83
 
0.9%
62
 
0.6%
42
 
0.6%
91
 
0.3%
Lowercase Letter
ValueCountFrequency (%)
n108
66.7%
a54
33.3%
Dash Punctuation
ValueCountFrequency (%)
-86
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common430
72.6%
Latin162
 
27.4%

Most frequent character per script

Common
ValueCountFrequency (%)
2140
32.6%
0113
26.3%
-86
20.0%
174
17.2%
36
 
1.4%
53
 
0.7%
83
 
0.7%
62
 
0.5%
42
 
0.5%
91
 
0.2%
Latin
ValueCountFrequency (%)
n108
66.7%
a54
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2140
23.6%
0113
19.1%
n108
18.2%
-86
14.5%
174
12.5%
a54
 
9.1%
36
 
1.0%
53
 
0.5%
83
 
0.5%
62
 
0.3%
Other values (2)3
 
0.5%

_embedded_show_officialSite
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct63
Distinct (%)64.9%
Missing0
Missing (%)0.0%
Memory size904.0 B
https://www.youtube.com/channel/UCVONgoOMPz54teymV2Jqwnw/videos
12 
nan
https://project-obsolete.com/en/
 
6
https://www.discoveryplus.co.uk/show/countdown-to-i-do
 
4
https://tv.nrk.no/serie/exit-nordpolen
 
3
Other values (58)
65 

Length

Max length97
Median length72
Mean length48.27835052
Min length3

Characters and Unicode

Total characters4683
Distinct characters74
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique51 ?
Unique (%)52.6%

Sample

1st rowhttps://www.vlive.tv/video/121637
2nd rowhttp://epic-media.ru/project/kotiki
3rd rowhttps://hd.kinopoisk.ru/film/41a971dd517a30f9b86d20def219c326
4th rowhttps://www.bilibili.com/bangumi/media/md28223064
5th rowhttps://v.qq.com/detail/m/7q544xyrava3vxf.html

Common Values

ValueCountFrequency (%)
https://www.youtube.com/channel/UCVONgoOMPz54teymV2Jqwnw/videos12
 
12.4%
nan7
 
7.2%
https://project-obsolete.com/en/6
 
6.2%
https://www.discoveryplus.co.uk/show/countdown-to-i-do4
 
4.1%
https://tv.nrk.no/serie/exit-nordpolen3
 
3.1%
https://v.qq.com/detail/m/mzc00200tu76tos.html2
 
2.1%
https://go.ocs.fr/details/serie/PSCHEYENNEEW01682592
 
2.1%
https://www.netflix.com/title/813372352
 
2.1%
https://v.qq.com/x/search/?q=+%E4%BB%8A%E5%A4%95%E4%BD%95%E5%A4%95&stag=0&smartbox_ab=2
 
2.1%
https://v.youku.com/v_show/id_XNDg2OTQ0ODAwOA==.html?s=dfbc7998206c499cac282
 
2.1%
Other values (53)55
56.7%

Length

2022-05-09T20:59:35.367300image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://www.youtube.com/channel/ucvongoompz54teymv2jqwnw/videos12
 
12.4%
nan7
 
7.2%
https://project-obsolete.com/en6
 
6.2%
https://www.discoveryplus.co.uk/show/countdown-to-i-do4
 
4.1%
https://tv.nrk.no/serie/exit-nordpolen3
 
3.1%
https://v.qq.com/x/search/?q=+%e4%bb%8a%e5%a4%95%e4%bd%95%e5%a4%95&stag=0&smartbox_ab2
 
2.1%
https://www.iqiyi.com/a_19rrhskr95.html2
 
2.1%
https://v.youku.com/v_show/id_xndg2otq0odawoa==.html?s=dfbc7998206c499cac282
 
2.1%
https://so.youku.com/search_video/q_%20%e6%9c%80%e5%88%9d%e7%9a%84%e7%9b%b8%e9%81%87?searchfrom=12
 
2.1%
https://www.netflix.com/title/813372352
 
2.1%
Other values (53)55
56.7%

Most occurring characters

ValueCountFrequency (%)
/382
 
8.2%
t364
 
7.8%
o259
 
5.5%
s229
 
4.9%
e229
 
4.9%
w214
 
4.6%
.178
 
3.8%
h151
 
3.2%
c150
 
3.2%
i142
 
3.0%
Other values (64)2385
50.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter3086
65.9%
Other Punctuation730
 
15.6%
Uppercase Letter396
 
8.5%
Decimal Number360
 
7.7%
Dash Punctuation65
 
1.4%
Math Symbol27
 
0.6%
Connector Punctuation19
 
0.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t364
 
11.8%
o259
 
8.4%
s229
 
7.4%
e229
 
7.4%
w214
 
6.9%
h151
 
4.9%
c150
 
4.9%
i142
 
4.6%
p141
 
4.6%
n133
 
4.3%
Other values (16)1074
34.8%
Uppercase Letter
ValueCountFrequency (%)
O38
 
9.6%
E32
 
8.1%
V29
 
7.3%
N28
 
7.1%
P27
 
6.8%
C26
 
6.6%
A22
 
5.6%
U20
 
5.1%
J20
 
5.1%
M15
 
3.8%
Other values (16)139
35.1%
Decimal Number
ValueCountFrequency (%)
251
14.2%
942
11.7%
441
11.4%
539
10.8%
038
10.6%
834
9.4%
131
8.6%
730
8.3%
628
7.8%
326
7.2%
Other Punctuation
ValueCountFrequency (%)
/382
52.3%
.178
24.4%
:90
 
12.3%
%57
 
7.8%
?16
 
2.2%
&5
 
0.7%
#1
 
0.1%
!1
 
0.1%
Math Symbol
ValueCountFrequency (%)
=25
92.6%
+2
 
7.4%
Dash Punctuation
ValueCountFrequency (%)
-65
100.0%
Connector Punctuation
ValueCountFrequency (%)
_19
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin3482
74.4%
Common1201
 
25.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
t364
 
10.5%
o259
 
7.4%
s229
 
6.6%
e229
 
6.6%
w214
 
6.1%
h151
 
4.3%
c150
 
4.3%
i142
 
4.1%
p141
 
4.0%
n133
 
3.8%
Other values (42)1470
42.2%
Common
ValueCountFrequency (%)
/382
31.8%
.178
14.8%
:90
 
7.5%
-65
 
5.4%
%57
 
4.7%
251
 
4.2%
942
 
3.5%
441
 
3.4%
539
 
3.2%
038
 
3.2%
Other values (12)218
18.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII4683
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/382
 
8.2%
t364
 
7.8%
o259
 
5.5%
s229
 
4.9%
e229
 
4.9%
w214
 
4.6%
.178
 
3.8%
h151
 
3.2%
c150
 
3.2%
i142
 
3.0%
Other values (64)2385
50.9%

_embedded_show_weight
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct42
Distinct (%)43.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.81443299
Minimum0
Maximum91
Zeros1
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size904.0 B
2022-05-09T20:59:35.489936image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q18
median22
Q337
95-th percentile77
Maximum91
Range91
Interquartile range (IQR)29

Descriptive statistics

Standard deviation22.65195443
Coefficient of variation (CV)0.814395693
Kurtosis0.3759349427
Mean27.81443299
Median Absolute Deviation (MAD)14
Skewness1.10231742
Sum2698
Variance513.1110395
MonotonicityNot monotonic
2022-05-09T20:59:35.591140image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
815
 
15.5%
67
 
7.2%
156
 
6.2%
346
 
6.2%
135
 
5.2%
184
 
4.1%
223
 
3.1%
553
 
3.1%
253
 
3.1%
323
 
3.1%
Other values (32)42
43.3%
ValueCountFrequency (%)
01
 
1.0%
21
 
1.0%
32
 
2.1%
67
7.2%
815
15.5%
91
 
1.0%
101
 
1.0%
111
 
1.0%
135
 
5.2%
143
 
3.1%
ValueCountFrequency (%)
911
1.0%
891
1.0%
811
1.0%
791
1.0%
772
2.1%
761
1.0%
711
1.0%
661
1.0%
651
1.0%
641
1.0%

_embedded_show_dvdCountry
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size904.0 B
nan
96 
{'name': 'Japan', 'code': 'JP', 'timezone': 'Asia/Tokyo'}
 
1

Length

Max length57
Median length3
Mean length3.556701031
Min length3

Characters and Unicode

Total characters345
Distinct characters25
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st rownan
2nd rownan
3rd rownan
4th rownan
5th rownan

Common Values

ValueCountFrequency (%)
nan96
99.0%
{'name': 'Japan', 'code': 'JP', 'timezone': 'Asia/Tokyo'}1
 
1.0%

Length

2022-05-09T20:59:35.712950image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T20:59:35.814915image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
nan96
94.1%
name1
 
1.0%
japan1
 
1.0%
code1
 
1.0%
jp1
 
1.0%
timezone1
 
1.0%
asia/tokyo1
 
1.0%

Most occurring characters

ValueCountFrequency (%)
n195
56.5%
a100
29.0%
'12
 
3.5%
5
 
1.4%
e4
 
1.2%
o4
 
1.2%
:3
 
0.9%
J2
 
0.6%
,2
 
0.6%
m2
 
0.6%
Other values (15)16
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter315
91.3%
Other Punctuation18
 
5.2%
Space Separator5
 
1.4%
Uppercase Letter5
 
1.4%
Open Punctuation1
 
0.3%
Close Punctuation1
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n195
61.9%
a100
31.7%
e4
 
1.3%
o4
 
1.3%
m2
 
0.6%
i2
 
0.6%
y1
 
0.3%
k1
 
0.3%
s1
 
0.3%
t1
 
0.3%
Other values (4)4
 
1.3%
Other Punctuation
ValueCountFrequency (%)
'12
66.7%
:3
 
16.7%
,2
 
11.1%
/1
 
5.6%
Uppercase Letter
ValueCountFrequency (%)
J2
40.0%
A1
20.0%
T1
20.0%
P1
20.0%
Space Separator
ValueCountFrequency (%)
5
100.0%
Open Punctuation
ValueCountFrequency (%)
{1
100.0%
Close Punctuation
ValueCountFrequency (%)
}1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin320
92.8%
Common25
 
7.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
n195
60.9%
a100
31.2%
e4
 
1.2%
o4
 
1.2%
J2
 
0.6%
m2
 
0.6%
i2
 
0.6%
A1
 
0.3%
y1
 
0.3%
k1
 
0.3%
Other values (8)8
 
2.5%
Common
ValueCountFrequency (%)
'12
48.0%
5
20.0%
:3
 
12.0%
,2
 
8.0%
/1
 
4.0%
{1
 
4.0%
}1
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII345
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n195
56.5%
a100
29.0%
'12
 
3.5%
5
 
1.4%
e4
 
1.2%
o4
 
1.2%
:3
 
0.9%
J2
 
0.6%
,2
 
0.6%
m2
 
0.6%
Other values (15)16
 
4.6%

_embedded_show_summary
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct59
Distinct (%)60.8%
Missing0
Missing (%)0.0%
Memory size904.0 B
<p>A 12-episode online series about the funny and touching adventures of a student blogger who promotes the "child free" movement, but is forced to go to work as a volunteer nanny.</p>
12 
nan
11 
<p>In 2014, aliens revealed themselves to request trade with humanity. In exchange for limestone, they would provide a consciousness-controlled general-use robot known as an "EXOFRAME". Cheaper than an aircraft, tank, or firearm, and easy enough for anyone to operate, the "EXOFRAME" spreads change throughout the world in the blink of an eye...</p>
 
6
<p>Married couples think back to their dream weddings a year later and discuss all the choices they made. Revisiting the stress, cost and chaos, will these couples think it was all worth it for just one day?</p>
 
4
<p>When Børge Ousland goes on a trip, it's serious. Last autumn, he crossed the Arctic Ocean on skis. Everything did not go exactly according to plan. What happened along the way? <b>Exit Nordpolen</b> takes you on an insane expedition.</p>
 
3
Other values (54)
61 

Length

Max length1084
Median length437
Mean length260.4329897
Min length3

Characters and Unicode

Total characters25262
Distinct characters89
Distinct categories10 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique47 ?
Unique (%)48.5%

Sample

1st row<p><b>Sim for You</b> is a reality series that chronicles each EXO member's life and reveal stories from their everyday life as a series.</p>
2nd rownan
3rd row<p>Russian music artists reveal themselves from unexpected sides in the Anton Belyaev's show.</p>
4th row<p>The power of beginnings, the energy of the core stone; one may find it good, one may find it evil. During a normal investigation, Yue Juntian finds himself drawn into the battle between the 'beginnings' of Yun City; Jiang Xin arrives in Yun City to stop Li Zunyuan's plan to take over. The two influence each other - one solves the mystery of their birth, the other redeems themselves. Together, they oppose Li Zunyuan.<br /> </p>
5th row<p>The protagonist Qin Chen, who was originally the top genius in the military domain, was conspired by the people to fall into the death canyon in the forbidden land of the mainland. Qin Chen, who was inevitably dead, unexpectedly triggered the power of the mysterious ancient sword.<br /><br />Three hundred years later, in a remote part of the Tianwu mainland, a boy of the same name accidentally inherited Qin Chen's will. As the beloved grandson of King Dingwu of the Daqi National Army, due to the birth father's birth, the mother and son were treated coldly in Dingwu's palace and lived together. In order to rewrite the myth of the strong man in hope of the sun, and to protect everything he loves, Qin Chen resolutely took up the responsibility of maintaining the five kingdoms of the world and set foot on the road of martial arts again.</p>

Common Values

ValueCountFrequency (%)
<p>A 12-episode online series about the funny and touching adventures of a student blogger who promotes the "child free" movement, but is forced to go to work as a volunteer nanny.</p>12
 
12.4%
nan11
 
11.3%
<p>In 2014, aliens revealed themselves to request trade with humanity. In exchange for limestone, they would provide a consciousness-controlled general-use robot known as an "EXOFRAME". Cheaper than an aircraft, tank, or firearm, and easy enough for anyone to operate, the "EXOFRAME" spreads change throughout the world in the blink of an eye...</p>6
 
6.2%
<p>Married couples think back to their dream weddings a year later and discuss all the choices they made. Revisiting the stress, cost and chaos, will these couples think it was all worth it for just one day?</p>4
 
4.1%
<p>When Børge Ousland goes on a trip, it's serious. Last autumn, he crossed the Arctic Ocean on skis. Everything did not go exactly according to plan. What happened along the way? <b>Exit Nordpolen</b> takes you on an insane expedition.</p>3
 
3.1%
<p>Merchant Jiang Shuo and his odd specialist companion Qin Yi Heng purchase frequented houses to exchange them. In any case, alarming things start to occur and each spooky house is by all accounts part of a major riddle. Jiang Shuo, Yi Heng, and police officer Yuan Mu Qing attempt to understand the riddle.</p>2
 
2.1%
<p>Cheyenne has been out of jail for six months now, working as a cleaner on the ferries whilst dreaming about traveling to the Amazon. Lola is a beautiful Parisian woman, selfish and ruthless, who has just arrived in the north of France to move in with her lover. But when Cheyenne witnesses Lola killing her lover's wife, she knows she's going to be accused of the crime because of her criminal past.</p>2
 
2.1%
<p>During the Yin Dynasty, Dong Yue, a brave general in the Dingyuan Rebellion, was sent back in time to stop a war that would claim the lives of countless innocents. She sets out to murder corrupted officer Lu Yuantong in an attempt to prevent war, and during her journey she met Feng Xi and Pang Yu. Pang Yu and Feng Xi were old friends who cared deeply for each other, but fell out and turn into enemies. While trying to reconcile the two brothers, Dong Yue also tries to stop Lu Yuantang's evil schemes which are poised to tear the nation apart with their help.</p>2
 
2.1%
<p>The police are investigating a case that involves a death directly caused by a rare bug known as the bullet ant. In order to clear his name, Tan Jingtian, an Insect toxicology graduate becomes involved in the bizzare investigation and collaborates with forensic doctor Jin Ling. As they dig deeper, they uncover the mystery behind his own identity.</p><p>Along with police captain Chen Han and the other detectives, they trace every clue as they solve one case at a time to uncover the murderer that has been in hiding for many years.</p>2
 
2.1%
<p>Pan, a desolate plastic surgeon, lived a repetitive and boring life every day until a conspiracy happened. He woke up in an abandoned factory, and found that someone had replaced his identity with a face exactly like him. His world has been completely overturned and left in a perilous situation. Can he overcome the difficulties and peel away the truth? How would he regain his identity?</p>2
 
2.1%
Other values (49)51
52.6%

Length

2022-05-09T20:59:36.040294image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the242
 
5.9%
and138
 
3.3%
to126
 
3.0%
a120
 
2.9%
of99
 
2.4%
in76
 
1.8%
with45
 
1.1%
is44
 
1.1%
as41
 
1.0%
an38
 
0.9%
Other values (1325)3165
76.6%

Most occurring characters

ValueCountFrequency (%)
4026
15.9%
e2476
 
9.8%
t1620
 
6.4%
n1504
 
6.0%
o1503
 
5.9%
a1472
 
5.8%
i1339
 
5.3%
s1252
 
5.0%
r1170
 
4.6%
h930
 
3.7%
Other values (79)7970
31.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter19085
75.5%
Space Separator4037
 
16.0%
Uppercase Letter761
 
3.0%
Other Punctuation740
 
2.9%
Math Symbol498
 
2.0%
Decimal Number72
 
0.3%
Dash Punctuation62
 
0.2%
Close Punctuation3
 
< 0.1%
Open Punctuation3
 
< 0.1%
Initial Punctuation1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e2476
13.0%
t1620
 
8.5%
n1504
 
7.9%
o1503
 
7.9%
a1472
 
7.7%
i1339
 
7.0%
s1252
 
6.6%
r1170
 
6.1%
h930
 
4.9%
l759
 
4.0%
Other values (23)5060
26.5%
Uppercase Letter
ValueCountFrequency (%)
A84
 
11.0%
T64
 
8.4%
E46
 
6.0%
I45
 
5.9%
M41
 
5.4%
C41
 
5.4%
H38
 
5.0%
L35
 
4.6%
R34
 
4.5%
F33
 
4.3%
Other values (17)300
39.4%
Other Punctuation
ValueCountFrequency (%)
,245
33.1%
.215
29.1%
/129
17.4%
"59
 
8.0%
'53
 
7.2%
?15
 
2.0%
!11
 
1.5%
:6
 
0.8%
4
 
0.5%
;3
 
0.4%
Decimal Number
ValueCountFrequency (%)
124
33.3%
224
33.3%
013
18.1%
46
 
8.3%
51
 
1.4%
61
 
1.4%
81
 
1.4%
71
 
1.4%
31
 
1.4%
Dash Punctuation
ValueCountFrequency (%)
-52
83.9%
9
 
14.5%
1
 
1.6%
Space Separator
ValueCountFrequency (%)
4026
99.7%
 11
 
0.3%
Math Symbol
ValueCountFrequency (%)
<249
50.0%
>249
50.0%
Close Punctuation
ValueCountFrequency (%)
)3
100.0%
Open Punctuation
ValueCountFrequency (%)
(3
100.0%
Initial Punctuation
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin19846
78.6%
Common5416
 
21.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e2476
12.5%
t1620
 
8.2%
n1504
 
7.6%
o1503
 
7.6%
a1472
 
7.4%
i1339
 
6.7%
s1252
 
6.3%
r1170
 
5.9%
h930
 
4.7%
l759
 
3.8%
Other values (50)5821
29.3%
Common
ValueCountFrequency (%)
4026
74.3%
<249
 
4.6%
>249
 
4.6%
,245
 
4.5%
.215
 
4.0%
/129
 
2.4%
"59
 
1.1%
'53
 
1.0%
-52
 
1.0%
124
 
0.4%
Other values (19)115
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII25223
99.8%
None24
 
0.1%
Punctuation15
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4026
16.0%
e2476
 
9.8%
t1620
 
6.4%
n1504
 
6.0%
o1503
 
6.0%
a1472
 
5.8%
i1339
 
5.3%
s1252
 
5.0%
r1170
 
4.6%
h930
 
3.7%
Other values (66)7931
31.4%
None
ValueCountFrequency (%)
 11
45.8%
ø5
20.8%
ü2
 
8.3%
á1
 
4.2%
å1
 
4.2%
Ç1
 
4.2%
ö1
 
4.2%
ş1
 
4.2%
é1
 
4.2%
Punctuation
ValueCountFrequency (%)
9
60.0%
4
26.7%
1
 
6.7%
1
 
6.7%

_embedded_show_updated
Real number (ℝ≥0)

HIGH CORRELATION

Distinct67
Distinct (%)69.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1631596432
Minimum1604587119
Maximum1651933209
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size904.0 B
2022-05-09T20:59:36.457085image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1604587119
5-th percentile1607713197
Q11613555245
median1634913215
Q31649705311
95-th percentile1651766819
Maximum1651933209
Range47346090
Interquartile range (IQR)36150066

Descriptive statistics

Standard deviation17017224.59
Coefficient of variation (CV)0.01042980007
Kurtosis-1.61854674
Mean1631596432
Median Absolute Deviation (MAD)15995585
Skewness-0.2060257723
Sum1.582648539 × 1011
Variance2.895859327 × 1014
MonotonicityNot monotonic
2022-05-09T20:59:36.644636image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
161355524512
 
12.4%
16307973046
 
6.2%
16211903614
 
4.1%
16129761163
 
3.1%
16518384632
 
2.1%
16340498902
 
2.1%
16090607262
 
2.1%
16508264802
 
2.1%
16064181642
 
2.1%
16095351412
 
2.1%
Other values (57)60
61.9%
ValueCountFrequency (%)
16045871191
1.0%
16064181642
2.1%
16076979652
2.1%
16077170051
1.0%
16083343021
1.0%
16083529671
1.0%
16084062791
1.0%
16084990071
1.0%
16090607262
2.1%
16095351412
2.1%
ValueCountFrequency (%)
16519332091
1.0%
16518386471
1.0%
16518384632
2.1%
16517773161
1.0%
16517641951
1.0%
16516154151
1.0%
16515911241
1.0%
16512614161
1.0%
16512534591
1.0%
16512532501
1.0%

_links_self_href
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct97
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size904.0 B
https://api.tvmaze.com/episodes/1977902
 
1
https://api.tvmaze.com/episodes/1996820
 
1
https://api.tvmaze.com/episodes/1949330
 
1
https://api.tvmaze.com/episodes/1949329
 
1
https://api.tvmaze.com/episodes/2176148
 
1
Other values (92)
92 

Length

Max length39
Median length39
Mean length39
Min length39

Characters and Unicode

Total characters3783
Distinct characters26
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique97 ?
Unique (%)100.0%

Sample

1st rowhttps://api.tvmaze.com/episodes/1977902
2nd rowhttps://api.tvmaze.com/episodes/2015818
3rd rowhttps://api.tvmaze.com/episodes/1964000
4th rowhttps://api.tvmaze.com/episodes/1995405
5th rowhttps://api.tvmaze.com/episodes/2007760

Common Values

ValueCountFrequency (%)
https://api.tvmaze.com/episodes/19779021
 
1.0%
https://api.tvmaze.com/episodes/19968201
 
1.0%
https://api.tvmaze.com/episodes/19493301
 
1.0%
https://api.tvmaze.com/episodes/19493291
 
1.0%
https://api.tvmaze.com/episodes/21761481
 
1.0%
https://api.tvmaze.com/episodes/21895551
 
1.0%
https://api.tvmaze.com/episodes/19986831
 
1.0%
https://api.tvmaze.com/episodes/19986821
 
1.0%
https://api.tvmaze.com/episodes/19986811
 
1.0%
https://api.tvmaze.com/episodes/19986801
 
1.0%
Other values (87)87
89.7%

Length

2022-05-09T20:59:36.804442image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://api.tvmaze.com/episodes/19779021
 
1.0%
https://api.tvmaze.com/episodes/20158181
 
1.0%
https://api.tvmaze.com/episodes/19640001
 
1.0%
https://api.tvmaze.com/episodes/19954051
 
1.0%
https://api.tvmaze.com/episodes/20077601
 
1.0%
https://api.tvmaze.com/episodes/19857891
 
1.0%
https://api.tvmaze.com/episodes/20396221
 
1.0%
https://api.tvmaze.com/episodes/20396231
 
1.0%
https://api.tvmaze.com/episodes/23244271
 
1.0%
https://api.tvmaze.com/episodes/23244281
 
1.0%
Other values (87)87
89.7%

Most occurring characters

ValueCountFrequency (%)
/388
 
10.3%
p291
 
7.7%
s291
 
7.7%
e291
 
7.7%
t291
 
7.7%
o194
 
5.1%
a194
 
5.1%
i194
 
5.1%
.194
 
5.1%
m194
 
5.1%
Other values (16)1261
33.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2425
64.1%
Other Punctuation679
 
17.9%
Decimal Number679
 
17.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
p291
12.0%
s291
12.0%
e291
12.0%
t291
12.0%
o194
8.0%
a194
8.0%
i194
8.0%
m194
8.0%
h97
 
4.0%
d97
 
4.0%
Other values (3)291
12.0%
Decimal Number
ValueCountFrequency (%)
9116
17.1%
2100
14.7%
189
13.1%
073
10.8%
361
9.0%
857
8.4%
651
7.5%
446
 
6.8%
744
 
6.5%
542
 
6.2%
Other Punctuation
ValueCountFrequency (%)
/388
57.1%
.194
28.6%
:97
 
14.3%

Most occurring scripts

ValueCountFrequency (%)
Latin2425
64.1%
Common1358
35.9%

Most frequent character per script

Common
ValueCountFrequency (%)
/388
28.6%
.194
14.3%
9116
 
8.5%
2100
 
7.4%
:97
 
7.1%
189
 
6.6%
073
 
5.4%
361
 
4.5%
857
 
4.2%
651
 
3.8%
Other values (3)132
 
9.7%
Latin
ValueCountFrequency (%)
p291
12.0%
s291
12.0%
e291
12.0%
t291
12.0%
o194
8.0%
a194
8.0%
i194
8.0%
m194
8.0%
h97
 
4.0%
d97
 
4.0%
Other values (3)291
12.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII3783
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/388
 
10.3%
p291
 
7.7%
s291
 
7.7%
e291
 
7.7%
t291
 
7.7%
o194
 
5.1%
a194
 
5.1%
i194
 
5.1%
.194
 
5.1%
m194
 
5.1%
Other values (16)1261
33.3%

Interactions

2022-05-09T20:59:25.951993image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T20:59:01.659108image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T20:59:06.585044image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T20:59:08.657905image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T20:59:11.620474image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T20:59:13.841065image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T20:59:18.301802image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T20:59:20.681159image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T20:59:23.135963image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T20:59:26.899946image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T20:59:02.883782image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T20:59:07.394458image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T20:59:10.085426image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T20:59:12.493076image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T20:59:15.216315image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T20:59:19.163036image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T20:59:21.586211image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T20:59:24.227183image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T20:59:27.023396image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T20:59:03.283769image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T20:59:07.490600image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T20:59:10.311882image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T20:59:12.607340image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T20:59:15.522408image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T20:59:19.445629image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T20:59:21.718842image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T20:59:24.326657image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T20:59:27.222424image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T20:59:03.696923image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T20:59:07.591842image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T20:59:10.434562image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T20:59:12.713502image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T20:59:15.879071image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T20:59:19.538802image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T20:59:21.836037image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T20:59:24.459060image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T20:59:27.378675image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T20:59:04.079764image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T20:59:07.702349image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T20:59:10.530886image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T20:59:12.829586image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T20:59:16.170391image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T20:59:19.645334image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T20:59:21.948572image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T20:59:24.604485image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T20:59:28.267126image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T20:59:04.954381image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T20:59:08.282209image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T20:59:11.161623image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T20:59:13.414945image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T20:59:17.049450image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T20:59:20.216510image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T20:59:22.590037image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T20:59:25.509675image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T20:59:28.377134image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T20:59:05.299378image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T20:59:08.374014image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T20:59:11.297042image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T20:59:13.529227image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T20:59:17.290684image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T20:59:20.325379image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T20:59:22.704052image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T20:59:25.624145image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T20:59:28.506185image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T20:59:05.811816image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T20:59:08.473288image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T20:59:11.400839image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T20:59:13.641455image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T20:59:17.615541image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T20:59:20.438974image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T20:59:22.821562image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T20:59:25.743478image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T20:59:28.623419image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T20:59:06.194179image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T20:59:08.560071image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T20:59:11.519934image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T20:59:13.741381image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T20:59:17.955160image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T20:59:20.567439image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T20:59:22.974529image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T20:59:25.843540image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-05-09T20:59:36.923534image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-05-09T20:59:37.126892image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-05-09T20:59:37.284308image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-05-09T20:59:37.568516image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-05-09T20:59:38.076187image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-05-09T20:59:28.946285image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-05-09T20:59:29.877315image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-05-09T20:59:30.217405image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-05-09T20:59:30.405835image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

idurlnameseasonnumbertypeairdateairtimeairstampruntimeimagesummary_embedded_show_id_embedded_show_url_embedded_show_name_embedded_show_type_embedded_show_language_embedded_show_genres_embedded_show_status_embedded_show_runtime_embedded_show_averageRuntime_embedded_show_premiered_embedded_show_ended_embedded_show_officialSite_embedded_show_weight_embedded_show_dvdCountry_embedded_show_summary_embedded_show_updated_links_self_href
01979824https://www.tvmaze.com/episodes/1979824/sim-for-you-4x16-chanyeols-episode-16Chanyeol's Episode 164.016.0regular2020-12-0106:002020-11-30T21:00:00+00:0016.0None<p><b>#ObtainedAConversationalSkill #WeSetUpATent♥ #ManySmiles</b></p>41648https://www.tvmaze.com/shows/41648/sim-for-youSim for YouRealityKorean[]Running16.016.02019-03-25nanhttps://www.vlive.tv/video/12163771.0nan<p><b>Sim for You</b> is a reality series that chronicles each EXO member's life and reveal stories from their everyday life as a series.</p>1.608499e+09https://api.tvmaze.com/episodes/1977902
11979222https://www.tvmaze.com/episodes/1979222/kotiki-1x02-seria-2Серия 21.02.0regular2020-12-01nan2020-12-01T00:00:00+00:0012.0Nonenan52198https://www.tvmaze.com/shows/52198/kotikiКотикиScriptedRussian['Comedy']Ended12.012.02020-11-302020-12-11http://epic-media.ru/project/kotiki15.0nannan1.637555e+09https://api.tvmaze.com/episodes/2015818
22008027https://www.tvmaze.com/episodes/2008027/lab-s-antonom-belaevym-2x06-lolitaЛолита2.06.0regular2020-12-01nan2020-12-01T00:00:00+00:0029.0{'medium': 'https://static.tvmaze.com/uploads/images/medium_landscape/294/737206.jpg', 'original': 'https://static.tvmaze.com/uploads/images/original_untouched/294/737206.jpg'}nan52933https://www.tvmaze.com/shows/52933/lab-s-antonom-belaevymLAB с Антоном БеляевымDocumentaryRussian['Music']Running26.025.02019-12-17nanhttps://hd.kinopoisk.ru/film/41a971dd517a30f9b86d20def219c32615.0nan<p>Russian music artists reveal themselves from unexpected sides in the Anton Belyaev's show.</p>1.641808e+09https://api.tvmaze.com/episodes/1964000
31964565https://www.tvmaze.com/episodes/1964565/core-sense-1x09-episode-9Episode 91.09.0regular2020-12-0110:002020-12-01T02:00:00+00:0024.0Nonenan51336https://www.tvmaze.com/shows/51336/core-senseCore SenseAnimationChinese['Action', 'Anime', 'Science-Fiction']Running24.024.02020-10-13nanhttps://www.bilibili.com/bangumi/media/md2822306437.0nan<p>The power of beginnings, the energy of the core stone; one may find it good, one may find it evil. During a normal investigation, Yue Juntian finds himself drawn into the battle between the 'beginnings' of Yun City; Jiang Xin arrives in Yun City to stop Li Zunyuan's plan to take over. The two influence each other - one solves the mystery of their birth, the other redeems themselves. Together, they oppose Li Zunyuan.<br /> </p>1.604587e+09https://api.tvmaze.com/episodes/1995405
42052503https://www.tvmaze.com/episodes/2052503/wu-shen-zhu-zai-1x80-episode-80Episode 801.080.0regular2020-12-0110:002020-12-01T02:00:00+00:008.0Nonenan54033https://www.tvmaze.com/shows/54033/wu-shen-zhu-zaiWu Shen Zhu ZaiAnimationChinese['Action', 'Adventure', 'Anime', 'Fantasy']Running8.08.02020-03-08nanhttps://v.qq.com/detail/m/7q544xyrava3vxf.html76.0nan<p>The protagonist Qin Chen, who was originally the top genius in the military domain, was conspired by the people to fall into the death canyon in the forbidden land of the mainland. Qin Chen, who was inevitably dead, unexpectedly triggered the power of the mysterious ancient sword.<br /><br />Three hundred years later, in a remote part of the Tianwu mainland, a boy of the same name accidentally inherited Qin Chen's will. As the beloved grandson of King Dingwu of the Daqi National Army, due to the birth father's birth, the mother and son were treated coldly in Dingwu's palace and lived together. In order to rewrite the myth of the strong man in hope of the sun, and to protect everything he loves, Qin Chen resolutely took up the responsibility of maintaining the five kingdoms of the world and set foot on the road of martial arts again.</p>1.649423e+09https://api.tvmaze.com/episodes/2007760
52315116https://www.tvmaze.com/episodes/2315116/sono-koi-mousukoshi-atatamemasuka-1x05-episode-5Episode 51.05.0regular2020-12-01nan2020-12-01T03:00:00+00:0015.0Nonenan61674https://www.tvmaze.com/shows/61674/sono-koi-mousukoshi-atatamemasukaSono koi Mousukoshi AtatamemasukaScriptedJapanese['Romance']Ended15.015.02020-10-202020-12-22https://www.paravi.jp/static/koisuko6.0nan<p>It's spin-off drama of <b>"Kono Koi Atatamemasu ka"</b></p>1.650915e+09https://api.tvmaze.com/episodes/1985789
61973538https://www.tvmaze.com/episodes/1973538/please-wait-brother-1x17-episode-17Episode 171.017.0regular2020-12-0112:002020-12-01T04:00:00+00:0037.0Nonenan52038https://www.tvmaze.com/shows/52038/please-wait-brotherPlease Wait, BrotherScriptedChinese['Comedy']Ended37.037.02020-11-172020-12-08nan14.0nannan1.607698e+09https://api.tvmaze.com/episodes/2039622
71973539https://www.tvmaze.com/episodes/1973539/please-wait-brother-1x18-episode-18Episode 181.018.0regular2020-12-0112:002020-12-01T04:00:00+00:0037.0Nonenan52038https://www.tvmaze.com/shows/52038/please-wait-brotherPlease Wait, BrotherScriptedChinese['Comedy']Ended37.037.02020-11-172020-12-08nan14.0nannan1.607698e+09https://api.tvmaze.com/episodes/2039623
81984264https://www.tvmaze.com/episodes/1984264/fearless-whispers-1x51-episode-51Episode 511.051.0regular2020-12-01nan2020-12-01T04:00:00+00:0060.0Nonenan52373https://www.tvmaze.com/shows/52373/fearless-whispersFearless WhispersScriptedChinese['Drama', 'Romance', 'History']Ended60.060.02020-11-062020-12-01nan15.0nan<p>A story revolving around a fresh graduate who holds an idealistic view of what's right and wrong, yet realizes that the very institution he chose to serve falls heavily onto a gray area caught in the struggles during chaotic times.</p>1.607717e+09https://api.tvmaze.com/episodes/2324427
92082171https://www.tvmaze.com/episodes/2082171/ling-jian-zun-4x28-di128ji第128集4.028.0regular2020-12-01nan2020-12-01T04:00:00+00:0010.0Nonenan55016https://www.tvmaze.com/shows/55016/ling-jian-zunLing Jian ZunAnimationChinese['Anime']Running10.010.02019-01-15nanhttps://v.qq.com/x/cover/2w2legt0g8z26al.html45.0nan<p>The strong man was attacked and returned to his youth. He became the weakest waste young lord. He will never let go of the enemy of the previous life in this life and must make up the regret of the previous life in this life! By the time the Spirit Sword is powerful, the protagonist will be supreme in the three worlds between heaven and earth! If there is someone doesn't obey him, he will kill him with the sword!</p><p><br /> </p>1.640703e+09https://api.tvmaze.com/episodes/2324428

Last rows

idurlnameseasonnumbertypeairdateairtimeairstampruntimeimagesummary_embedded_show_id_embedded_show_url_embedded_show_name_embedded_show_type_embedded_show_language_embedded_show_genres_embedded_show_status_embedded_show_runtime_embedded_show_averageRuntime_embedded_show_premiered_embedded_show_ended_embedded_show_officialSite_embedded_show_weight_embedded_show_dvdCountry_embedded_show_summary_embedded_show_updated_links_self_href
871976871https://www.tvmaze.com/episodes/1976871/nfl-films-presents-2020-12-01-sights-and-sounds-of-successSights and Sounds of Success2020.013.0regular2020-12-0108:302020-12-01T13:30:00+00:0030.0Nonenan6441https://www.tvmaze.com/shows/6441/nfl-films-presentsNFL Films PresentsSportsEnglish[]Running30.030.01999-08-23nanhttp://www.nfl.com/videos/nfl-films-presents26.0nan<p><b>NFL Films Presents</b> is devoted to producing commercials, television programs, feature films, and documentaries on the National Football League, as well as other unrelated major events and awards shows. It is currently owned by the NFL and produces most of its videotaped content except its live game coverage, which is handled separately by the individual networks."</p>1.651026e+09https://api.tvmaze.com/episodes/1985483
882311017https://www.tvmaze.com/episodes/2311017/toki-wo-kakeru-bando-1x07-chahhan-saneCHAHHAN SANE1.07.0regular2020-12-0100:252020-12-01T15:25:00+00:0026.0{'medium': 'https://static.tvmaze.com/uploads/images/medium_landscape/403/1009865.jpg', 'original': 'https://static.tvmaze.com/uploads/images/original_untouched/403/1009865.jpg'}<p>After the audition, 'Yuki Ebana' was confessed to 'Seiichi Izumi'. 'Shiori Kato' and 'Hitoko Murakami' witnessed it, and Hitoko, who had been thinking about Seiichi, screamed and ran away, and Shiori chased it.<br />On the other hand, for some reason, 'Ryo' will be drunk with Seiichi's band members 'Taki Hiroto' and 'Tsubaki Aoi', and will be re-drinked at Taki and Taki's house. Taki tells Ryo that he is thinking of leaving the band and becoming a doctor. Ryo presses the taiko stamp that Taki can be compatible, but Taki is not serious. Taki notices that something is wrong with Ryo's body, but Ryo tells others to keep silent.<br />In Seiichi's confession, Yuki and Hitoko were jerky. Meanwhile, the president 'Yuichi Yanagishita' called and told that the major debut of 'Chahhan' was decided. Three people who rejoice.<br />However, when I went to the office a few days later, I found that the debut song was not the original song, but a song made by a stranger ('77'). The three are not convinced, but Yuki remembers something stuck in the lyrics of 'No. 77' ...</p>61530https://www.tvmaze.com/shows/61530/toki-wo-kakeru-bandoToki wo Kakeru BandoScriptedJapanese['Comedy', 'Music', 'Science-Fiction']Ended27.026.02020-10-202020-12-22https://www.fujitv.co.jp/tokikake/0.0{'name': 'Japan', 'code': 'JP', 'timezone': 'Asia/Tokyo'}<p>A story about Ryo, a mysterious and self-proclaimed music producer from the future, producing a girl band of three girls and leading them to stardom. A comical and tempo conversational drama, and various trials to produce the youth of young people who play music with comedy touch.</p>1.649705e+09https://api.tvmaze.com/episodes/1985484
892165005https://www.tvmaze.com/episodes/2165005/all-about-android-2020-12-01-android-ostracizationAndroid Ostracization2020.048.0regular2020-12-01nan2020-12-01T17:00:00+00:0090.0Nonenan17633https://www.tvmaze.com/shows/17633/all-about-androidAll About AndroidNewsEnglish[]Running90.090.02011-03-29nanhttps://twit.tv/shows/all-about-android30.0nan<p><b>All About Android </b>delivers everything you want to know about Android each week -- the biggest news, freshest hardware, best apps and geekiest how-to's -- with Android enthusiasts Jason Howell, Florence Ion, Ron Richards, and a variety of special guests along the way.</p>1.650312e+09https://api.tvmaze.com/episodes/1988405
901963910https://www.tvmaze.com/episodes/1963910/a-teacher-1x06-episode-6Episode 61.06.0regular2020-12-01nan2020-12-01T17:00:00+00:0024.0{'medium': 'https://static.tvmaze.com/uploads/images/medium_landscape/284/710176.jpg', 'original': 'https://static.tvmaze.com/uploads/images/original_untouched/284/710176.jpg'}<p>As Claire's world begins to crumble, an uncertain Eric is pressed to reveal intimate details of his relationship.</p>38339https://www.tvmaze.com/shows/38339/a-teacherA TeacherScriptedEnglish['Drama']EndedNaN27.02020-11-102020-12-29https://www.hulu.com/series/a-teacher-1c871218-05b1-4c66-a22f-260b2cb9bbf991.0nan<p><b>A Teacher</b> examines the complexities and consequences of an illegal relationship between a female teacher, Claire and her male high school student, Eric. Dissatisfied in their own lives, Claire and Eric discover an undeniable escape in each other, but their relationship accelerates faster than anticipated and the permanent damage becomes impossible to ignore.</p>1.637345e+09https://api.tvmaze.com/episodes/1997537
911977755https://www.tvmaze.com/episodes/1977755/tooning-out-the-news-1x105-inside-the-hill"Inside the Hill"1.0105.0regular2020-12-01nan2020-12-01T17:00:00+00:007.0None<p>Inside The Hill breaks down Trump's voter fraud conspiracy theories, Biden's cabinet picks, and Senate candidate Kelly Loeffler's campaign ad with guest Rep. Linda Sanchez (D-CA).</p>45812https://www.tvmaze.com/shows/45812/tooning-out-the-newsTooning Out the NewsAnimationEnglish['Comedy']RunningNaN13.02020-04-07nanhttps://www.paramountplus.com/shows/tooning-out-the-news/43.0nan<p><b>Tooning Out the News</b> will provide short daily segments leading up to a weekly full episodes featuring a cast of animated characters mocking news of the day, and interviewing real-world guests and newsmakers.</p>1.636748e+09https://api.tvmaze.com/episodes/1997538
921979393https://www.tvmaze.com/episodes/1979393/i-was-a-teenage-felon-1x09-the-k2-kingpinThe K2 Kingpin1.09.0regular2020-12-01nan2020-12-01T17:00:00+00:0060.0{'medium': 'https://static.tvmaze.com/uploads/images/medium_landscape/359/897951.jpg', 'original': 'https://static.tvmaze.com/uploads/images/original_untouched/359/897951.jpg'}<p>K2 is a mysterious and extremly destructive drug that 18 yr old Yazz not only abused but also trafficked, earning him 100k a week while also causing him to spiral out of control.</p>50661https://www.tvmaze.com/shows/50661/i-was-a-teenage-felonI Was a Teenage FelonDocumentaryEnglish['Crime']Running60.060.02020-09-22nanhttps://video.vice.com/en_us/show/i-was-a-teenage-felon41.0nan<p>Former criminals tell the true tales of their rollercoaster ride from average American kids to wildly successful outlaws.</p>1.637763e+09https://api.tvmaze.com/episodes/2000072
931960028https://www.tvmaze.com/episodes/1960028/goede-tijden-slechte-tijden-31x54-aflevering-6309Aflevering 630931.054.0regular2020-12-0120:002020-12-01T19:00:00+00:0023.0{'medium': 'https://static.tvmaze.com/uploads/images/medium_landscape/285/712958.jpg', 'original': 'https://static.tvmaze.com/uploads/images/original_untouched/285/712958.jpg'}nan2504https://www.tvmaze.com/shows/2504/goede-tijden-slechte-tijdenGoede Tijden, Slechte TijdenScriptedDutch['Drama', 'Romance']Running23.025.01990-10-01nanhttp://gtst.nl/#!/77.0nannan1.651839e+09https://api.tvmaze.com/episodes/2000073
941976929https://www.tvmaze.com/episodes/1976929/cheyenne-et-lola-1x03-les-chacalsLes chacals1.03.0regular2020-12-0120:402020-12-01T19:40:00+00:0050.0{'medium': 'https://static.tvmaze.com/uploads/images/medium_landscape/289/724603.jpg', 'original': 'https://static.tvmaze.com/uploads/images/original_untouched/289/724603.jpg'}<p>Cheyenne découvre qu'un policier ripou dont elle ignore l'identité est l'informateur de Yannick.</p>50106https://www.tvmaze.com/shows/50106/cheyenne-et-lolaCheyenne et LolaScriptedFrench['Drama', 'Comedy', 'Crime']Running50.050.02020-11-24nanhttps://go.ocs.fr/details/serie/PSCHEYENNEEW016825940.0nan<p>Cheyenne has been out of jail for six months now, working as a cleaner on the ferries whilst dreaming about traveling to the Amazon. Lola is a beautiful Parisian woman, selfish and ruthless, who has just arrived in the north of France to move in with her lover. But when Cheyenne witnesses Lola killing her lover's wife, she knows she's going to be accused of the crime because of her criminal past.</p>1.644651e+09https://api.tvmaze.com/episodes/2001665
951976930https://www.tvmaze.com/episodes/1976930/cheyenne-et-lola-1x04-eldoradoEldorado1.04.0regular2020-12-0120:402020-12-01T19:40:00+00:0050.0{'medium': 'https://static.tvmaze.com/uploads/images/medium_landscape/289/724604.jpg', 'original': 'https://static.tvmaze.com/uploads/images/original_untouched/289/724604.jpg'}<p>Cheyenne se fait passer pour le bras droit de l'Anglais auprès d'un passeur nigérian.</p>50106https://www.tvmaze.com/shows/50106/cheyenne-et-lolaCheyenne et LolaScriptedFrench['Drama', 'Comedy', 'Crime']Running50.050.02020-11-24nanhttps://go.ocs.fr/details/serie/PSCHEYENNEEW016825940.0nan<p>Cheyenne has been out of jail for six months now, working as a cleaner on the ferries whilst dreaming about traveling to the Amazon. Lola is a beautiful Parisian woman, selfish and ruthless, who has just arrived in the north of France to move in with her lover. But when Cheyenne witnesses Lola killing her lover's wife, she knows she's going to be accused of the crime because of her criminal past.</p>1.644651e+09https://api.tvmaze.com/episodes/2001666
961978451https://www.tvmaze.com/episodes/1978451/chicken-girls-7x13-breakfast-clubBreakfast Club7.013.0regular2020-12-0115:002020-12-01T20:00:00+00:0016.0{'medium': 'https://static.tvmaze.com/uploads/images/medium_landscape/369/923709.jpg', 'original': 'https://static.tvmaze.com/uploads/images/original_untouched/369/923709.jpg'}<p>In detention, the girls put aside their differences. </p>32087https://www.tvmaze.com/shows/32087/chicken-girlsChicken GirlsScriptedEnglish['Drama', 'Children', 'Music']RunningNaN14.02017-09-05nanhttps://www.youtube.com/playlist?list=PLVewHiZp3_LPhqzbcZFmS3iuDm9HymTsy81.0nan<p>Rhyme and her friends — known by their 'ship name, "The Chicken Girls" — have been dancing together forever. But this year, everything's changing...</p>1.650395e+09https://api.tvmaze.com/episodes/2001667